Summary Aligning Large Language Models for Information Retrieval arxiv.org
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One Line
The RLCF framework helps language models improve their specificity for information retrieval tasks.
Slides
Slide Presentation (9 slides)
Key Points
- Large language models (LLMs) often generate responses that lack specificity in information retrieval (IR) tasks.
- The authors propose an unsupervised alignment framework called Reinforcement Learning from Contrastive Feedback (RLCF) to address this issue.
- RLCF enables LLMs to generate high-quality and context-specific responses that suit the needs of IR tasks.
- The RLCF framework involves constructing contrastive feedback by comparing each document with its similar documents.
- RLCF optimizes LLMs through reinforcement learning using the Proximal Policy Optimization algorithm.
- The experimental results show that RLCF effectively improves the performance of LLMs in an IR context.
- RLCF-optimized LLMs outperform vanilla LLMs in data augmentation and document summarization tasks.
- RLCF optimization aligns the capabilities of LLMs with the context of information retrieval, resulting in more specific summaries and queries for documents.
Summaries
17 word summary
The RLCF framework aligns large language models with information retrieval tasks by addressing their lack of specificity.
60 word summary
The Reinforcement Learning from Contrastive Feedback (RLCF) framework aligns large language models (LLMs) with information retrieval (IR) tasks. RLCF addresses the issue of LLMs lacking specificity by enabling them to generate context-specific responses suitable for IR. The framework involves constructing contrastive feedback using the Batched-MRR reward function. Popular applications and the effectiveness of RLCF are discussed and demonstrated through experiments.
117 word summary
The Reinforcement Learning from Contrastive Feedback (RLCF) framework is introduced in this paper to align large language models (LLMs) with information retrieval (IR) tasks. RLCF addresses the issue of LLMs lacking specificity in their responses by enabling them to generate high-quality and context-specific responses suitable for IR. The framework involves constructing contrastive feedback by comparing each document with its similar documents using the Batched-MRR reward function. The limitations of LLMs in IR are discussed, and popular applications such as data augmentation and document summarization are presented. The RLCF framework optimizes LLMs through reinforcement learning using the Proximal Policy Optimization algorithm. Experiments demonstrate the effectiveness of RLCF in improving LLM performance in dense retrieval tasks and document summarization.
394 word summary
The paper introduces the Reinforcement Learning from Contrastive Feedback (RLCF) framework, which aligns large language models (LLMs) with information retrieval (IR) tasks. LLMs often lack specificity in their responses, limiting their effectiveness in IR. RLCF addresses this issue by enabling LLMs to generate high-quality and context-specific responses suitable for IR.
RLCF involves constructing contrastive feedback by comparing each document with its similar documents. The authors use the Batched-MRR reward function to teach LLMs to generate responses that capture fine-grained distinctions between documents. Experiments in data augmentation and summarization tasks demonstrate the effectiveness of RLCF in improving LLM performance in IR.
The limitations of LLMs in IR, including hallucination and slow knowledge update, are discussed. Misalignment between LLM capabilities and IR needs is identified as a key problem. Popular applications of LLMs in IR, such as data augmentation and document summarization, are presented.
The training pipeline of LLMs, including pre-training, supervised fine-tuning (SFT), and alignment stages, is discussed. However, the existing training pipeline fails to ensure the capability of LLMs to differentiate fine-grained distinctions in information.
To address this issue, the authors propose the RLCF framework, an unsupervised framework that utilizes contrastive feedback to align LLMs with IR context and capture fine-grained distinctions within documents. The framework includes contrastive data construction, RLCF optimization, and calculation of contrastive feedback.
The RLCF framework optimizes LLMs through reinforcement learning using the Proximal Policy Optimization (PPO) algorithm. The Batched-MRR is considered as the reward score, and the PPO algorithm maximizes this reward score. A penalty term is incorporated in the reward to prevent significant divergence from the vanilla LLM.
Experiments on various datasets demonstrate the effectiveness of RLCF in improving LLM performance in IR. The main contributions of the study are proposing the RLCF framework, introducing the Batched-MRR metric, and demonstrating the effectiveness of the framework through comprehensive experiments.
Experiments on document summarization tasks show that RLCF optimization significantly improves Rouge-diff scores on both Chinese and English datasets. Experiments on dense retrieval tasks show that RLCF-optimized LLMs consistently outperform vanilla LLMs in terms of evaluation metrics such as MRR@10, Recall@20, Recall@100, and NDCG@10. The effect of data augmentation increases with the number of parameters in LLMs.
In conclusion, the study introduces the RLCF framework, which leverages contrastive feedback to optimize LLMs. The experiments demonstrate the effectiveness of RLCF in improving LLM performance in dense retrieval tasks and document summarization.
566 word summary
The paper introduces the Reinforcement Learning from Contrastive Feedback (RLCF) framework, which aims to align large language models (LLMs) with the context of information retrieval (IR). LLMs have shown impressive capabilities in various tasks, but they often lack specificity in their responses, limiting their effectiveness in IR. RLCF addresses this issue by enabling LLMs to generate high-quality and context-specific responses that are suitable for IR tasks.
The RLCF framework involves constructing contrastive feedback by comparing each document with its similar documents. The authors use a reward function called Batched-MRR to teach LLMs to generate responses that capture the fine-grained information that distinguishes documents from their similar ones. The authors conducted experiments in data augmentation and summarization tasks to demonstrate the effectiveness of RLCF in improving the performance of LLMs in an IR context.
The paper discusses the limitations of LLMs in IR, including hallucination and slow knowledge update, which hinder their reliability as information accessing tools. The misalignment between the capabilities of LLMs and the needs of IR tasks is identified as a key problem. The paper presents examples of popular applications of LLMs in IR, namely data augmentation and document summarization.
The training pipeline of LLMs, which includes pre-training, supervised fine-tuning (SFT), and alignment stages, is discussed. However, the existing training pipeline fails to ensure the capability of LLMs to differentiate fine-grained distinctions in information.
To address this issue, the authors propose the RLCF framework, an unsupervised framework that utilizes contrastive feedback to align LLMs with IR context and capture fine-grained distinctions within documents. The framework includes contrastive data construction, RLCF optimization, and the calculation of contrastive feedback.
The RLCF framework optimizes LLMs through reinforcement learning using the Proximal Policy Optimization (PPO) algorithm. The Batched-MRR is considered as the reward score, and the PPO algorithm maximizes this reward score. A penalty term is also incorporated in the reward to prevent the policy model from producing responses that diverge significantly from the vanilla LLM.
Experiments conducted on various datasets demonstrate the effectiveness of RLCF in improving the performance of LLMs in IR context. The authors summarize their main contributions as proposing the RLCF framework, introducing the Batched-MRR metric, and demonstrating the effectiveness of the framework through comprehensive experiments.
The study focuses on document summarization for vanilla LLMs and RLCF-optimized LLMs. The experiments are conducted on two datasets: LCSTS for Chinese and Gigaword for English. The results show that RLCF-optimized LLMs consistently outperform vanilla LLMs in terms of NDCG@10, Recall@100, and Batched-MRR metrics. The experiments also include document summarization tasks, where RLCF optimization significantly improves the Rouge-diff scores on both Chinese and English datasets.
The study proposes a novel framework called RLCF that leverages contrastive feedback to optimize large language models. The experiments demonstrate the effectiveness of RLCF in improving the performance of LLMs in dense retrieval tasks and document summarization.
The experiments on dense retrieval tasks involve various datasets such as MS-MARCO, NQ, TriviaQ, and BEIR. The results show that RLCF-optimized LLMs consistently outperform vanilla LLMs in terms of MRR@10, Recall@20, Recall@100, NDCG@10, and other evaluation metrics. The study also analyzes the scaling law of LLMs on data augmentation for dense retrieval and finds that the effect of data augmentation increases with the number of parameters in LLMs.
The experiments on document summarization tasks involve LCSTS and Gigaword datasets. The results show that RLCF optimization significantly improves the Rouge-diff scores on both datasets, indicating the effectiveness
1031 word summary
The paper discusses the alignment of large language models (LLMs) with the context of information retrieval (IR) through contrastive feedback. LLMs have shown remarkable capabilities in various tasks, but they often generate responses that lack specificity, limiting their effectiveness in IR. To address this issue, the authors propose an unsupervised alignment framework called Reinforcement Learning from Contrastive Feedback (RLCF). RLCF enables LLMs to generate high-quality and context-specific responses that suit the needs of IR tasks.
The RLCF framework involves constructing contrastive feedback by comparing each document with its similar documents. A reward function called Batched-MRR is used to teach LLMs to generate responses that capture the fine-grained information that distinguishes documents from their similar ones. The authors conducted experiments in two typical applications of LLMs in IR, namely data augmentation and summarization, to demonstrate the effectiveness of RLCF. The experimental results show that RLCF can effectively improve the performance of LLMs in an IR context.
The paper highlights the importance of IR in modern society and the potential of LLMs to support or empower IR systems. It discusses the limitations of LLMs in IR, including hallucination and slow knowledge update, which prevent them from serving as reliable information accessing tools. The misalignment between the capabilities of LLMs and the general needs of IR tasks, particularly the capability to differentiate fine-grained distinctions in documents, is identified as a key problem. The paper presents two example cases of popular applications of LLMs in IR: data augmentation and document summarization.
The training pipeline of LLMs, which includes pre-training, supervised fine-tuning (SFT), and alignment stages, is discussed. The pre-training stage equips LLMs with linguistic knowledge from a massive corpus, while the SFT stage focuses on training LLMs to support different types of instructions and prompts with supervised data. The alignment stage aims to align the capabilities of LLMs with environmental feedback. However, the existing training pipeline fails to ensure the capability of LLMs to differentiate fine-grained distinctions in information.
To align the capability of LLMs with IR context, the authors propose the RLCF framework. RLCF is a novel unsupervised framework that utilizes contrastive feedback to align LLMs with IR context and capture fine-grained distinctions within documents without supervision. The framework includes contrastive data construction, RLCF optimization, and the calculation of contrastive feedback. Contrastive feedback is obtained through the comparison of similar documents using a retriever.
The RLCF framework optimizes LLMs through reinforcement learning, specifically with the Proximal Policy Optimization (PPO) algorithm. The Batched-MRR is considered as the reward score for the entire response, and the PPO algorithm maximizes this reward score. A penalty term is also incorporated in the reward to prevent the policy model from producing responses that diverge significantly from the vanilla LLM.
The authors conducted experiments on BEIR, MS-MARCO, NQ, and TriviaQA datasets to evaluate the effectiveness of RLCF in data augmentation and document summarization tasks. The experimental results demonstrate the effectiveness of RLCF in improving the performance of LLMs in IR context. The authors summarize their main contributions as proposing the RLCF framework, introducing the Batched-MRR metric, and demonstrating the effectiveness of the framework through comprehensive experiments.
Overall, the paper presents a novel framework for aligning LLMs with the context of IR through contrastive feedback. The RLCF framework shows promise in improving the specificity and effectiveness of responses generated by LLMs in IR tasks such as data augmentation and document summarization.
The study focuses on the effectiveness of document summarization for vanilla Large Language Models (LLMs) and LLMs optimized using Reinforcement Learning with Contrastive Feedback (RLCF). The experiments are conducted on two datasets: LCSTS for Chinese and Gigaword for English. LCSTS is a dataset used for short text summarization in Chinese, while Gigaword is a large-scale collection of news articles and their summaries. The implementation details include the use of Flan-T5 as the backbone of LLMs for English datasets and BELLE-7B-2M for the Chinese dataset. The experiments involve data augmentation for dense retrieval tasks such as question answering, entity retrieval, and fact checking. The results show that RLCF-optimized LLMs consistently outperform vanilla LLMs in terms of NDCG@10, Recall@100, and Batched-MRR metrics. The experiments also include document summarization tasks, where RLCF optimization significantly improves the Rouge-diff scores on both Chinese and English datasets. Human evaluation further confirms that summaries generated by RLCF-optimized LLMs are more specific and effective in distinguishing similar documents compared to vanilla LLMs. The study concludes that RLCF optimization aligns the capabilities of LLMs with the context of information retrieval, resulting in more specific summaries and queries for documents.
The study proposes a novel framework called RLCF that leverages contrastive feedback to optimize large language models. The framework involves constructing groups of similar documents, feeding them into LLMs, obtaining responses, and calculating contrastive feedback using a reward function called Batched-MRR. The contrastive feedback is then used to optimize LLMs using the Proximal Policy Optimization algorithm. The experiments demonstrate the effectiveness of RLCF in improving the performance of LLMs in dense retrieval tasks and document summarization.
The experiments on dense retrieval tasks involve various datasets such as MS-MARCO, NQ, TriviaQ, and BEIR. The results show that RLCF-optimized LLMs consistently outperform vanilla LLMs in terms of MRR@10, Recall@20, Recall@100, NDCG@10, and other evaluation metrics. The study also analyzes the scaling law of LLMs on data augmentation for dense retrieval and finds that the effect of data augmentation increases with the number of parameters in LLMs.
The experiments on document summarization tasks involve LCSTS and Gigaword datasets. The results show that RLCF optimization significantly improves the Rouge-diff scores on both datasets, indicating the effectiveness of RLCF in generating more specific and informative summaries. Human evaluation further confirms the superiority of summaries generated by RLCF-optimized LLMs over vanilla LLMs.
The study concludes by suggesting future directions for research, such as exploring other domains for RLCF optimization and incorporating explicit knowledge in pre-trained language models for passage re-ranking. The references provide additional resources for further reading on related topics.
Overall, the study demonstrates the effectiveness of RLCF optimization in aligning the capabilities of large language models with the context of information retrieval. The experiments on dense retrieval and document summarization tasks show significant improvements in performance when using RLCF-optimized LLMs compared to vanilla LLMs.