Summary Modeling Ambiguity in Language Understanding arxiv.org
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The document discusses the modeling of ambiguity in language understanding and proposes the AmbiNLI model to address the issue, evaluating multilabel NLI models and creating a dataset called AMBIENT to evaluate the ability of language models to recognize and disentangle possible meanings.
Key Points
- Ambiguity in language can lead to miscommunication and confusion, making ambiguity-sensitive tools important for natural language processing and human understanding.
- A multilabel natural language inference (NLI) model can be used to detect misinformation in political claims and the value of ambiguity recognition.
- The AmbiNLI model is proposed to address ambiguity in natural language understanding.
- The study evaluates the ability of Language Models (LMs) to generate disambiguations and recognize plausible interpretations, showing that LMs can effectively model ambiguity in language understanding.
- The authors encourage future work to collect more data in other languages and to systematically extend the dataset and analyses.
- The importance of recognizing interpretation-specific contexts and disambiguations is highlighted.
Summaries
285 word summary
This document explores the modeling of ambiguity in language understanding with a focus on generating paraphrases for political claims. The authors use the InstructGPT model and sample 5 outputs per 21,273 possible prompts to construct a taxonomy of ambiguity categories and review examples from their dataset. They emphasize the importance of recognizing interpretation-specific contexts and disambiguations and develop a benchmark to evaluate language models' sensitivity to context. The authors also experimentally evaluate the idea of modeling ambiguity on political claims using the development set of CLAIM DECOMP and illustrate the value of ambiguity-sensitive models in detecting misleading political claims. The document includes references to various studies and evaluations related to natural language processing and language understanding, covering topics such as multi-task learning, semantic evaluation, language modeling, annotation, ambiguity, and bias detection. The AmbiNLI model is proposed to address ambiguity in natural language understanding. The study evaluates multilabel NLI models and proposes a method to quantify the likelihood of a continuation given a distractor sentence. The document explores modeling ambiguity in language understanding through a three-step process of annotating ambiguous examples, recognizing disambiguations, and selecting a single label. The study evaluates the ability of Language Models (LMs) to generate disambiguations and recognize plausible interpretations. Additionally, the authors use the KL divergence to measure the impact of ambiguous contexts on LMs and propose a few-shot template for generating disambiguations. A dataset called AMBIENT was created to evaluate the ability of language models to recognize and disentangle possible meanings. The authors used a pipeline to annotate and validate examples acquired from a corpus of unlabeled NLI examples that are likely to be ambiguous. Finally, they analyzed the ambiguous examples in their dataset and collected disambiguations labeled by linguists.
829 word summary
Ambiguity in language can lead to miscommunication and confusion, making ambiguity-sensitive tools important for natural language processing and human understanding. A study investigated the use of a multilabel natural language inference (NLI) model to detect misinformation in political claims and the value of ambiguity recognition. A dataset called AMBIENT was created to evaluate the ability of language models to recognize and disentangle possible meanings. The authors used a pipeline to annotate and validate examples acquired from a corpus of unlabeled NLI examples that are likely to be ambiguous. The dataset includes 1,645 examples, each annotated with a set of labels indicating whether a premise entails, contradicts, or is neutral with respect to a hypothesis. The authors also identified groups of premise-hypothesis pairs that share a reasoning pattern to encourage the creation of new examples with the same pattern. Finally, they analyzed the ambiguous examples in their dataset and collected disambiguations labeled by linguists. The document explores modeling ambiguity in language understanding through a three-step process of annotating ambiguous examples, recognizing disambiguations, and selecting a single label. The study evaluates the ability of Language Models (LMs) to generate disambiguations and recognize plausible interpretations, showing that LMs can effectively model ambiguity in language understanding. The authors also discuss a method for modeling ambiguity in determining whether a claim is true, false, or inconclusive given a premise, with the best model achieving an E DIT-F1 score of 18.0%. Additionally, the authors use the KL divergence to measure the impact of ambiguous contexts on LMs and propose a few-shot template for generating disambiguations, with the best model achieving 63% accuracy. The study proposes the AmbiNLI model to address ambiguity in natural language understanding. Multilabel NLI models are evaluated using various methods and datasets, and the results show that ambiguity remains a severe challenge across models and tests. The study also proposes a method to quantify the likelihood of a continuation given a distractor sentence and evaluates it on various language models.
The authors experimentally evaluate the idea of modeling ambiguity on political claims using the development set of CLAIM DECOMP. They use a multilabel NLI model to assign at least two labels to each resulting NLI example and then paraphrase each claim five times with InstructGPT zero-shot. They illustrate the value of ambiguity-sensitive models in detecting misleading political claims.
The authors train three-way classification models on the single-label train sets of MNLI and WANLI and train a multilabel model on the power set of NLI labels, minus the empty set. They also train a classifier over sets which performs 7-way classification over the annotations per example. The multilabel model trained on WANLI achieves the highest macro F1 score of 37.8%.
Ambiguity in language understanding is a long-standing issue, and recent work studies whether the confidence of coreference and NLI models is sensitive to ambiguities more broadly. The functional approach to ambiguity is inspired by AMBIG QA, and pretrained LMs are evaluated for the language model to solve highly ambiguous crossword clues. Political claims flagged as ambiguous by the detection method are shown in Table 6. The document discusses modeling ambiguity in language understanding and the need for ambiguity-sensitive tools to address systematic biases. The authors develop a benchmark to evaluate language models' sensitivity to context and emphasize the importance of studying the nuances of natural language communication. They also investigate different approaches to studying label variation in natural language inference (NLI) and recognize the growing interest in ambiguity-sensitive tools for various applications. The document includes references to various studies and evaluations related to natural language processing, such as coping with syntactic ambiguity and scaling up pretraining. The authors encourage future work to collect more data in other languages and to systematically extend the dataset and analyses. The text also includes a list of references and resources related to natural language processing and language understanding, covering topics such as multi-task learning, semantic evaluation, language modeling, annotation, ambiguity, and bias detection. The document examines modeling ambiguity in language understanding through disambiguation examples. The authors use heuristics to obtain 104,071 unlabeled examples and sample 5 outputs per 21,273 possible prompts. They employ InstructGPT as the model and provide curated examples and dataset creation details. The authors construct a taxonomy of ambiguity categories, review examples from their dataset, and annotate 100 randomly sampled examples to categorize possible sources of ambiguity. The study finds that the “closeness” of distractors affects the difficulty of a test and highlights the importance of recognizing interpretation-specific contexts and disambiguations. This document focuses on the modeling of ambiguity in language understanding, specifically in generating paraphrases for political claims. The InstructGPT model was used and trained on a dataset of political claims over 30 epochs. The AmbiNLI model was found to require no threshold tuning for performance evaluation using logit thresholds. Additionally, the setup of NLI models that predict multiple labels as output is described. However, the document notes that the noun replacement procedure used in some tests may not always produce accurate results.
2347 word summary
This document discusses the modeling of ambiguity in language understanding, specifically in generating paraphrases for political claims. The model used is InstructGPT, trained on a dataset of political claims over 30 epochs. The performance of various models is evaluated using logit thresholds, and the AmbiNLI model is found to require no threshold tuning. The setup of NLI models that predict multiple labels as output is also described. The document notes that the noun replacement procedure used in some tests may not always produce accurate results. This document discusses modeling ambiguity in language understanding. The study finds that the "closeness" of distractors affects the difficulty of a test. There can be a stylistic mismatch between the original ambiguous sentence and its disambiguation, with the latter being more stilted. The document describes the process of generating continuations and creating distractors. The KL divergence is used to measure the difference between two probability distributions. Implementation details and test results are provided. The study highlights the importance of recognizing interpretation-specific contexts and disambiguations. The document discusses modeling ambiguity in language understanding. The claim presented in the document has multiple interpretations that affect its correctness. The accuracy of LMs on four templates is presented in Table 10. The claim has ambiguities, and the premise and hypothesis are often ambiguous. The study used crowdworkers to assess the plausibility of three interpretations of an ambiguous sentence. The workers were paid $0.40 per NLI example, and only those who passed a qualification test were selected. The study found that the distribution of ambiguity in naturally occurring language is not uniform, and some sentences have multiple ambiguities. The paper focuses on modeling ambiguity in language understanding. The authors construct a taxonomy of ambiguity categories by reviewing examples from their dataset. They annotate 100 randomly sampled examples and categorize the possible sources of ambiguity. The authors review all 2,020 examples and validate the annotations. Among the ambiguous examples, 74.3% have ambiguity in the premise and 32.6% in the hypothesis. The disambiguating rewrites are, on average, 2.36 words longer than their ambiguous counterparts. The paper demonstrates shared ambiguity patterns where sentences about the past or desires about the future induce a cancellable implicature about the present. The authors provide a prompt template for GPT-3 used to create un-labeled examples for annotation. They ultimately focus on linguistic ambiguity and provide a table of ambiguity categories. The document discusses modeling ambiguity in language understanding. It includes examples of sentences that have changed meaning due to the use of different economic systems. The document also discusses the process of generating and annotating examples for a multilabel NLI model, which was used to filter examples based on certain rules. The resulting set of examples was used to test the model's ability to predict the relationship between a premise and a hypothesis. The document includes a table with examples from various sources that were used in the study. The article discusses modeling ambiguity in language understanding through disambiguation examples. The authors employ heuristics to discard examples that exhibit observable failure cases and obtain a total of 104,071 unlabeled examples. They sample 5 outputs per 21,273 possible prompts and discard the output if the generated output is not correctly formatted with max tokens 120 and stop sequence ”/n/n”. The model used is InstructGPT. The article provides curated examples and dataset creation details, including references to related research. This text excerpt is a list of references and resources related to natural language processing and language understanding. It includes mentions of various tools, benchmarks, and models used in the field, as well as research papers and conference proceedings. The referenced materials cover topics such as multi-task learning, semantic evaluation, language modeling, annotation, ambiguity, and bias detection. Some of the highlighted resources are specific models or algorithms, such as Blenderbot 3 or GPT-4, while others are datasets or evaluation frameworks, such as GLUE or SemEval-2021. The text also mentions several authors and researchers who have contributed to the field of natural language processing, such as Samuel Bowman or Catherine Havasi. Overall, the text serves as a resource list for anyone interested in exploring the current state of language understanding research. This is a list of various academic papers and conference proceedings related to natural language processing, including topics such as ambiguity in language understanding, natural language inference, human-AI collaboration, and machine learning models. The papers cover a range of subtopics within these areas, such as exploring language model capabilities, investigating reasons for disagreement in natural language inference, and integrating dissenting voices into machine learning models. Some specific papers mentioned include "What can we learn from collective human opinions on linguistics?", "No language left behind: Scaling human-centered machine translation", and "The curious case of neural text degeneration". The document discusses modeling ambiguity in language understanding. It includes references to various studies and evaluations, such as the use of HPSG for English grammar, coping with syntactic ambiguity, and scaling up pretraining. The authors note that while larger language models may overfit to more common interpretations, scaling up pretraining and reinforcement learning from human feedback may lead to further gains. The authors also point out that while LMs struggle with ambiguity in English, the way ambiguity manifests in other languages can vary greatly due to systematic typological factors or idiosyncratic differences. They encourage future work to collect more data in other languages and to systematically extend the dataset and analyses. This work aims to collect a broad-coverage dataset of ambiguities to model ambiguity in language understanding. The authors acknowledge the limitations of existing models due to data sources and the need for ambiguity-sensitive tools to address systematic biases. They develop a benchmark to evaluate language models' sensitivity to context and emphasis and encourage future work to study the nuances of natural language communication. The authors investigate different approaches to studying label variation in natural language inference (NLI) and develop a set of labels for plausible readings. They argue that uncertainty in sentence meaning should be directly characterized, potentially as a function of demographic characteristics. The authors recognize the growing interest in ambiguity-sensitive tools for various applications, such as toxic language detection. Ambiguity in language understanding is a long-standing and well-studied issue for NLP tasks involving symbolic analyses of sentences, such as syntactic and semantic parsing. Recent work studies whether the confidence of coreference and NLI models is sensitive to ambiguities more broadly, whose resolution is a prerequisite to understanding meaning. The task ambiguity arises when the task is underspecified, subjectivity of annotation, and input ambiguity. Our functional approach to ambiguity, where the ambiguity in task input is disambiguated in natural language to account for variation in possible outputs, is inspired by A MBIG QA. Going beyond analysis and evaluation of task modeling, we evaluate pretrained LMs for the language model to solve highly ambiguous crossword clues. We find this approach enables a flexible and explainable way of representing ambiguity. Table 6 shows political claims flagged as ambiguous by our detection method. Namely, a generated paraphrase (shown in the hypothesis column) happens to be disambiguating, thus leading the multilabel NLI model to predict multiple labels. The document discusses a method for modeling ambiguity in language understanding. The authors experimentally evaluate the idea on the development set of CLAIM DECOMP, using political claims as a case study. They use a multilabel NLI model to assign at least two labels to each resulting NLI example and then paraphrase each claim five times with InstructGPT zero-shot. They read each instance of ambiguity or factuality and mark whether the fact-check describes an issue of ambiguity. The authors illustrate the value of ambiguity-sensitive models in detecting misleading political claims. They train three-way classification models on the single-label train sets of MNLI and WANLI and train a multilabel model on the power set of NLI labels, minus the empty set. They also train a classifier over sets which performs 7-way classification over the annotations per example. The multilabel model trained on WANLI achieves the highest macro F1 score of 37.8%. While this is substantially higher than the random-guessing baseline of 1/7 = 14.3% for EM accuracy, it is considerably short of 89.7% human accuracy. The study explores the challenge of ambiguity in natural language understanding and proposes the use of the AmbiNLI model to address this issue. The study evaluates multilabel NLI models using various methods such as regression, classification, and distributional models on datasets like W A NLI, MNLI, and Uncertain NLI. The study also experiments with predicting a single set of labels or a probability value for ambiguous examples. The results show that ambiguity remains a severe challenge across models and tests. The study suggests that performance on ambiguity is heavily dependent on performance in other settings, and the inconsistent trends suggest the need for further investigation. The study also proposes a method to quantify the likelihood of a continuation given a distractor sentence and evaluates it on various language models. This document discusses a method for modeling ambiguity in language understanding. The authors use the KL divergence to measure the impact of ambiguous contexts on language models (LMs). They sample continuations for each interpretation and compare their likelihoods under the ambiguous sentence and corresponding disambiguation. The authors also propose a few-shot template for generating disambiguations and evaluate the performance of pretrained models on this task. The best model achieves 63% accuracy. The document discusses modeling ambiguity in language understanding, specifically in determining whether a claim is true, false, or inconclusive given a premise. The process involves selecting model-predicted NLI labels and considering plausible disambiguations based on majority vote. Human evaluation is also conducted using the same setup, with the F1 metric used to represent disambiguation. Different interpretations of the context can lead to different judgments about the claim, and recognizing disambiguations can be challenging. One strategy involves restating the premise to clarify ambiguity. The best model achieves an E DIT-F1 score of 18.0%. This article discusses the modeling of ambiguity in language understanding. The study evaluates whether Language Models (LMs) can learn to generate disambiguations and recognize the validity of plausible interpretations. The LMs evaluated include ChatGPT, InstructGPT, FLAN-T5, GPT-3, and LLaMa. The experiment tests the ability of LMs to directly generate relevant disambiguations and recognize the full set of ambiguities in a given input. The study shows that input ambiguity is a source of disagreement among annotators, and that individual agreement is largely resolved on the corresponding disambiguated examples. The results demonstrate that LMs can learn to generate disambiguations and recognize plausible interpretations through pretraining. Furthermore, the study shows that back-translation can be used to generate semantically similar distractors and that annotators overwhelmingly recognize possible interpretations. Overall, the study suggests that LMs can effectively model ambiguity in language understanding. The document discusses modeling ambiguity in language understanding. The process involves three steps: annotation of ambiguous examples, recognition of disambiguations, and selection of a single label. Each example is reviewed by 9 workers, and inter-annotator agreement is calculated. The types of ambiguity present include lexical, syntactic, figurative, pragmatic, scopal, coreference, and other. A final dataset is created by combining curated and generated-then-annotated examples. Annotators may discard examples themselves, and the validation phase is performed by a subset of linguistics students. The authors review the annotations to select a set of labels for each example, including the singleton set when the example is unambiguous. The document discusses modeling ambiguity in language understanding. The authors use a pipeline to annotate and validate examples acquired from a corpus of unlabeled NLI examples that are likely to be ambiguous. The authors use a multilabel RoBERTa-large model trained on WANLI and retain all examples where the model assigns probability ≥0.05 to more than one label. They further filter for likely-ambiguous instances, such as sentences indicating at least slight uncertainty in NLI label or containing interpretable ambiguity patterns, such as sentences with differing pragmatic and literal readings. They use overgeneration and filtering to automatically create a large corpus of labeled premise-hypothesis pairs, which they directly annotate with the set of labels and disambiguations. The authors also identify groups of premise-hypothesis pairs that share a reasoning pattern, to encourage the creation of new examples with the same pattern. Finally, they analyze the ambiguous examples in their dataset and collect disambiguations labeled by linguists. The article discusses the creation of a dataset called AMBIENT, which contains examples of ambiguous language in natural language inference (NLI). The dataset includes 1,645 examples, each annotated with a set of labels indicating whether a premise entails, contradicts, or is neutral with respect to a hypothesis. The authors used two approaches to collect source examples: manual curation and automatic generation. The inclusion of ambiguous examples facilitates evaluating model abilities to first detect the presence of relevant ambiguity, then resolve it to distinct interpretations. The article highlights the promise of tools to aid real-world communication in dealing with ambiguous claims. A study investigated the use of a multilabel NLI model to detect misinformation in leading political claims. The study also explored the value of ambiguity recognition and whether LMs can distinguish between different interpretations of ambiguous sentences. A suite of tests was designed to characterize ambiguity, including manual curation and a functional approach using natural language meaning representation. The ability to recognize ambiguity in language can lead to clearer communication and more effective writing aids. Pretrained LMs can aid in identifying misleading or deceptive language and aid in human communication. Ambiguity is a common feature of language, with multiple interpretations possible based on contextual factors. This can lead to unintended miscommunication and confusion, requiring listeners to ask clarifying questions and communicators to anticipate ambiguity. A multilabel natural language inference model can be used to flag potentially misleading political claims. Ambiguity-sensitive tools are important for natural language processing and human language understanding, and managing ambiguity is a key part of language comprehension. Language models need to better model ambiguity, which can be extremely challenging due to the diverse kinds of ambiguity present in natural language. A linguist-annotated benchmark called A MBI E NT can be used to evaluate the ability of language models to recognize and disentangle possible meanings.