Summary R-Tuning Teaching Large Language Models to Refuse Unknown Questions arxiv.org
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One Line
R-Tuning is a technique that assesses the knowledge limitations of large language models, pinpoints areas of uncertainty, teaches them to decline queries they are unsure about, and enhances their performance on tasks they are knowledgeable about.
Slides
Slide Presentation (12 slides)
Key Points
- Hallucination, the propensity of large language models (LLMs) to generate non-existent facts, is a predominant issue with these models
- The significant gap between the knowledge in human-labeled instruction tuning datasets and the parametric knowledge of LLMs is a major cause of hallucination
- The proposed Refusal-Aware Instruction Tuning (R-Tuning) method identifies uncertain questions that are beyond the model's knowledge, and constructs a refusal-aware dataset to teach the model to express uncertainty when faced with such questions
- The refusal ability learned by R-Tuning is found to be a generalizable meta-skill that benefits from multi-task training
- Incorporating uncertainty learning into large model training can improve both the model's ability to estimate uncertainty and its overall accuracy
Summaries
20 word summary
R-Tuning measures knowledge gaps, identifies uncertain questions, and teaches LLMs to refuse unknown queries while improving accuracy on known tasks.
48 word summary
R-Tuning measures the knowledge gap between pre-trained LLMs and instruction data, identifying uncertain questions. It appends uncertainty expressions to these questions, teaching the model to refuse unknown queries while improving accuracy on known tasks. Experiments show the refusal ability generalizes across tasks and is enhanced through multi-task training.
122 word summary
This paper presents R-Tuning, a novel approach to address hallucination in large language models (LLMs). R-Tuning first measures the knowledge gap between the pre-trained model and the instruction tuning data, identifying uncertain questions beyond the model's knowledge. It then constructs a refusal-aware dataset by appending uncertainty expressions to uncertain questions, while keeping original labels for certain questions. This teaches the model to express uncertainty when faced with unknown questions, rather than hallucinating answers. Experiments show R-Tuning enables the model to refuse uncertain questions while improving accuracy on questions it can answer. Importantly, the refusal ability generalizes across tasks and is further enhanced through multi-task training. The authors suggest incorporating uncertainty learning into LLM training can improve both uncertainty estimation and overall accuracy.
329 word summary
This paper presents a novel approach called Refusal-Aware Instruction Tuning (R-Tuning) to address the hallucination issue in large language models (LLMs). The key insight is that the significant gap between the knowledge in human-labeled instruction tuning datasets and the parametric knowledge of LLMs is a major cause of hallucination.
R-Tuning consists of two main steps. First, it measures the knowledge gap between the pre-trained model and the instruction tuning data, identifying uncertain questions that are beyond the model's knowledge. This is done by comparing the model's predictions on the training data with the ground-truth labels. Questions where the prediction matches the label are considered "certain", while mismatched questions are "uncertain".
Second, R-Tuning constructs a refusal-aware dataset by appending uncertainty expressions to the uncertain questions, while keeping the original labels for the certain questions. This teaches the model to express uncertainty when faced with questions outside its knowledge boundary, rather than hallucating answers.
The authors' experiments on diverse datasets show that R-Tuning enables the model to refuse uncertain questions while improving accuracy on the questions it is willing to answer, compared to traditional fine-tuning. Importantly, the refusal ability learned by R-Tuning is found to be a "meta-skill" that generalizes across tasks and is further enhanced through multi-task training.
A key finding is that learning uncertainty during training, rather than just applying uncertainty filtering at test time, yields better results. This suggests that incorporating uncertainty learning into large model training can improve both the model's ability to estimate uncertainty and its overall accuracy. Further analysis reveals that uncertain questions have higher perplexity and entropy in the model's predictions, explaining why R-Tuning is effective at distinguishing them.
The authors also explore variants of R-Tuning, demonstrating the flexibility and effectiveness of the core approach. Overall, this work takes an important step towards building more reliable and trustworthy large language models that can better recognize the limits of their own knowledge, with broad implications for improving the safety and robustness of next-generation AI systems.
471 word summary
Refusal-Aware Instruction Tuning (R-Tuning) for Large Language Models
This paper presents a novel approach called Refusal-Aware Instruction Tuning (R-Tuning) to address the hallucination issue in large language models (LLMs). The key insight is that the significant gap between the knowledge in human-labeled instruction tuning datasets and the parametric knowledge of LLMs is a major cause of hallucination.
R-Tuning consists of two main steps. First, it measures the knowledge gap between the parametric knowledge of the pre-trained model and the instruction tuning data, and identifies uncertain questions that are beyond the model's knowledge. This is done by comparing the model's predictions on the training data with the ground-truth labels. Questions where the prediction matches the label are considered "certain", while mismatched questions are "uncertain".
Second, R-Tuning constructs a refusal-aware dataset by appending uncertainty expressions (e.g., "I am unsure") to the uncertain questions, while keeping the original labels for the certain questions. This teaches the model to express uncertainty when faced with questions outside its knowledge boundary, rather than hallucating answers.
The authors conduct experiments on both single-task and multi-task settings, evaluating on 7 diverse datasets. The results show that R-Tuning enables the model to refuse to answer uncertain questions, while improving the accuracy on the questions it is willing to answer, compared to traditional fine-tuning approaches. Importantly, the refusal ability learned by R-Tuning is found to be a "meta-skill" that generalizes across tasks, and is further enhanced through multi-task training.
A key finding is that learning uncertainty during training, rather than just applying uncertainty filtering at test time, yields better results. This suggests that incorporating uncertainty learning into large model training can improve both the model's ability to estimate uncertainty and its overall accuracy. Further analysis reveals that uncertain questions have higher perplexity and entropy in the model's predictions, explaining why R-Tuning is effective at distinguishing them.
The authors also explore variants of R-Tuning, including an unsupervised identification strategy and a label replacement method. These alternatives demonstrate the flexibility and effectiveness of the core R-Tuning approach.
In summary, the main contributions of this work are:
1. Identifying the knowledge gap between instruction tuning data and parametric knowledge as a key cause of hallucination in LLMs. 2. Proposing the R-Tuning method to teach LLMs to refuse unknown questions by distinguishing certain and uncertain data during instruction tuning. 3. Showing that the refusal ability learned by R-Tuning is a generalizable meta-skill that benefits from multi-task training. 4. Discovering the advantages of incorporating uncertainty learning into large model training, both in reducing computational overhead and improving overall model accuracy.
Overall, this work takes an important step towards building more reliable and trustworthy large language models that can better recognize the limits of their own knowledge. The insights and techniques developed here could have broad implications for improving the safety and robustness of next-generation AI systems.
832 word summary
R-Tuning: Teaching Large Language Models to Refuse Unknown Questions Hanning Zhang ♠∗ , Shizhe Diao ♠∗ , Yong Lin ♠∗ , Yi R. Fung ♡ , Qing Lian ♠ , Xingyao Wang ♡ , Yangyi Chen ♡ , Heng Ji ♡ , Tong Zhang ♠ ♠ The Hong Kong University of Science and Technology ♡ University of Illinois Urbana-Champaign {hzhangco, sdiaoaa, ylindf, qlianab, tongzhang}@ust.hk {yifung2, xingyao6, yangyic3, hengji}@illinois.edu Abstract Large language models (LLMs) have revolu- tionized numerous domains with their impres- sive performance but still face their challenges. A predominant issue is the propensity for these models to generate non-existent facts, a con- cern termed hallucination. Our research is mo- tivated by the observation that previous instruc- tion tuning methods force the model to com- plete a sentence no matter whether the model knows the knowledge or not. When the ques- tion is out of the parametric knowledge, it will try to make up something and fail to indicate when it lacks knowledge. In this paper, we present a new approach called Refusal-Aware Instruction Tuning (R-Tuning). This approach is formalized by first identifying the knowl- edge gap between parametric knowledge and the instruction tuning data. Then, we construct the refusal-aware data based on the knowledge intersection, to tune LLMs to refrain from re- sponding to questions beyond its parametric knowledge. Experimental results demonstrate this new instruction tuning approach effectively improves a model’s ability to answer known questions and refrain from answering unknown questions. Furthermore, when tested on out-of- domain datasets, the refusal ability was found to be a meta-skill that could be generalized to other tasks. Further analysis surprisingly finds that learning the uncertainty during training displays a better ability to estimate uncertainty than uncertainty-based testing. 1 1 Introduction Large language models (LLMs) have demonstrated remarkable performance across numerous tasks; however, they are also plagued by various issues, such as the propensity of large models to fabricate non-existent facts, a phenomenon commonly re- ferred to as hallucination (Maynez et al., 2020a). * Equal Contribution. Our code will be released at https://github.com/ shizhediao/R-Tuning. 1 Parametric Knowledge Instruction Tuning Data [What Model Already Knows] [What Model Might Not Know] Intersection of
Refusal-Aware Instruction Tuning (R-Tuning) for Large Language Models
This paper proposes a novel instruction tuning method called Refusal-Aware Instruction Tuning (R-Tuning) to address the hallucination issue in large language models (LLMs). The key insight is that the significant gap between the knowledge in human-labeled instruction tuning datasets and the parametric knowledge of LLMs is a major cause of hallucination.
R-Tuning consists of two main steps. First, it measures the knowledge gap between the parametric knowledge of the pre-trained model and the instruction tuning data, and identifies uncertain questions that are beyond the model's knowledge. This is done by comparing the model's predictions on the training data with the ground-truth labels. Questions where the prediction matches the label are considered "certain", while mismatched questions are "uncertain".
Second, R-Tuning constructs a refusal-aware dataset by appending uncertainty expressions (e.g., "I am unsure") to the uncertain questions, while keeping the original labels for the certain questions. This teaches the model to express uncertainty when faced with questions outside its knowledge boundary, rather than hallucating answers.
The authors conduct experiments on both single-task and multi-task settings, evaluating on 7 diverse datasets. The results show that R-Tuning enables the model to refuse to answer uncertain questions, while improving the accuracy on the questions it is willing to answer, compared to traditional fine-tuning approaches. Importantly, the refusal ability learned by R-Tuning is found to be a "meta-skill" that generalizes across tasks, and is further enhanced through multi-task training.
A key finding is that learning uncertainty during training, rather than just applying uncertainty filtering at test time, yields better results. This suggests that incorporating uncertainty learning into large model training can improve both the model's ability to estimate uncertainty and its overall accuracy. Further analysis reveals that uncertain questions have higher perplexity and entropy in the model's predictions, explaining why R-Tuning is effective at distinguishing them.
The authors also explore variants of R-Tuning, including an unsupervised identification strategy and a label replacement method. These alternatives demonstrate the flexibility and effectiveness of the core R-Tuning approach.
In summary, the main contributions of this work are:
1. Identifying the knowledge gap between instruction tuning data and parametric knowledge as a key cause of hallucination in LLMs.
2. Proposing the R-Tuning method to teach LLMs to refuse unknown questions by distinguishing certain and uncertain data during instruction tuning.
3. Showing that the refusal ability learned by R-Tuning is a generalizable meta-skill that benefits from multi-task training.
4. Discovering the advantages of incorporating uncertainty learning into large model training, both in reducing computational overhead and improving overall model accuracy.
Overall, this work takes an important step towards building more reliable and trustworthy large language models that can better recognize the limits of their own knowledge. The insights and techniques developed here could have broad implications for improving the safety and robustness of next-generation AI systems.