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Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models
Wenhao Yu, Hongming Zhang, Xiaoman Pan, Kaixin Ma, Hongwei Wang, Dong Yu
TL;DR
RALMs can be misled by irrelevant retrieved documents and lack a reliable way to recognize insufficient knowledge. CON generates sequential notes to assess document relevance and synthesize answers, and experiments report stronger robustness than standard RALMs, including gains under noisy and unknown-question settings.
Problem
RALMs may be misguided by irrelevant retrievals, overlook intrinsic knowledge, or fail to answer “unknown” when available knowledge is insufficient.
Method
CON generates sequential reading notes for retrieved documents, evaluates their relevance and reliability, and synthesizes them into the final answer.
Results
RALMs equipped with CON significantly outperform standard fine-tuned RALMs across four open-domain QA benchmarks, including +7.9 EM on fully noisy documents and +10.5 rejection rate for real-time questions.
Takeaways & Limitations
CON improves RALM robustness to noisy retrieved documents and questions beyond the model’s pre-training knowledge scope.
Takeaways & Limitations
CON increases inference cost and response time, while efficiency depends on the conciseness and relevance of generated notes.
Abstract
from arXiv · showhide
Retrieval-augmented language models (RALMs) represent a substantial advancement in the capabilities of large language models, notably in reducing factual hallucination by leveraging external knowledge sources. However, the reliability of the retrieved information is not always guaranteed. The retrieval of irrelevant data can lead to misguided responses, and potentially causing the model to overlook its inherent knowledge, even when it possesses adequate information to address the query. Moreover, standard RALMs often struggle to assess whether they possess adequate knowledge, both intrinsic and retrieved, to provide an accurate answer. In situations where knowledge is lacking, these systems should ideally respond with "unknown" when the answer is unattainable. In response to these challenges, we introduces Chain-of-Noting (CoN), a novel approach aimed at improving the robustness of RALMs in facing noisy, irrelevant documents and in handling unknown scenarios. The core idea of CoN is to generate sequential reading notes for retrieved documents, enabling a thorough evaluation of their relevance to the given question and integrating this information to formulate the final answer. We employed ChatGPT to create training data for CoN, which was subsequently trained on an LLaMa-2 7B model. Our experiments across four open-domain QA benchmarks show that RALMs equipped with CoN significantly outperform standard RALMs. Notably, CoN achieves an average improvement of +7.9 in EM score given entirely noisy retrieved documents and +10.5 in rejection rates for real-time questions that fall outside the pre-training knowledge scope.
1 Introduction
RALMs use retrieved external knowledge to improve language-model answers, but irrelevant or insufficient evidence can mislead models and obscure when they should answer “unknown.” CHAIN-OF-NOTE (CON) addresses these issues by generating sequential document notes before answering and improves robustness across noise and unknown-question settings.
- Existing RALMs: RALMs combine retrieval with language-model reading to use external documents when formulating answers.The retriever searches an evidence corpus, and the reader extracts useful information for the final output.
- Existing RALMs: Irrelevant retrieved information can produce misguided responses or cause models to overlook adequate inherent knowledge.The retrieval system is not guaranteed to return pertinent or trustworthy information.
- Robustness challenges: RALMs should distinguish noise robustness from unknown robustness: disregarding irrelevant documents while using intrinsic knowledge, and answering “unknown” when knowledge is insufficient.These two capabilities define the paper’s robustness targets.
- Proposed framework: CON generates sequential reading notes for retrieved documents to assess relevance before synthesizing a final response.The approach also identifies critical information and can guide an “unknown” response when retrieved documents are uninformative.
- Results: +7.9 EM score improvement with fully noisy retrieved documents and +10.5 rejection-rate improvement for real-time questions beyond pre-training knowledge were reported.The evaluation covered NQ and three additional out-of-domain open-domain QA datasets.
2 Proposed Method
CON restructures retrieval-augmented answering around sequential notes that evaluate documents before producing a final response. The method also uses GPT-4-generated training data to train LLaMa-2 7B, while hybrid training targets the inference-cost drawback of explicit notes.
- Background of Existing RALMs: Standard RALMs approximate document marginalization with the k highest-ranked retrieved documents supplied to the generator.The query is x, the response is y, and retrieved documents are represented as d_1 through d_k.
- Background of Existing RALMs: Direct answer generation risks shallow document processing, contradiction handling, limited interpretability, and overdependence on retrieved content.These limitations motivate structured intermediate notes.
- CHAIN-OF-NOTE Framework: CON generates a reading-note segment for each retrieved document and then synthesizes those segments into a consolidated final response.Its implementation comprises note design, data collection, and model training.
- CHAIN-OF-NOTE Framework: CON uses three note cases: direct answers rely on retrieved information, contextual documents are combined with inherent knowledge, and irrelevant documents can yield “unknown.”The cases adapt processing to document relevance and the model’s knowledge boundaries.
- Training: GPT-4 generated 10K CON training examples from sampled NQ questions, which were used to train LLaMa-2 7B to generate notes and answers.The training prompt concatenates the instruction, question, and documents.
- Efficiency: Hybrid Training splits training evenly between standard RALM and CON examples, enabling standard-prompt inference without explicit notes.The resulting model maintains the same inference time with slightly lower performance than explicit CON.
3 Experiments
Experiments compare CHAIN-OF-NOTE with retrieve-read baselines across open-domain QA, noisy retrieval, unknown questions, and decoding efficiency. CON improves overall and robustness performance, with especially large gains under fully noisy retrieval and unknown scenarios.
- Experimental Setup: Experiments use NQ, TriviaQA, WebQ, and RealTimeQA, with DPR-retrieved documents and full-set or robustness-focused subset evaluations.RealTimeQA is used specifically for unknown robustness, while questions without relevant DPR documents are excluded from robustness evaluation.
- Methods: CHAIN-OF-NOTE generates a reading note for each retrieved document before producing the final answer.Unlike retrieve-read models that map questions and documents directly to answers, CON outputs document-level notes plus the answer.
- Overall QA Performance: +1.97 EM is the average improvement across three datasets when CON is compared with standard RALM using LLaMa-2.The improvement is +1.2 when DPR retrieves relevant documents and +2.3 when it does not on NQ.
- Noise Robustness: +7.9 EM is the average improvement on fully noisy documents across three open-domain QA datasets.CON consistently improves performance across lower noise ratios and is especially effective when retrieved documents are entirely noisy.
- Unknown Robustness: +10.5 is the improvement in rejection rate for unknown questions on RealTimeQA compared with standard RALM.RealTimeQA contains real-time information outside LLaMa-2 pre-training, and reject rate is rejected questions divided by total questions.
- Efficiency: The hybrid training strategy retains similar decoding-time consumption to standard RALM while showing slightly lower robustness across noise ratios.This strategy trains note generation but uses the standard RALM prompt during inference, leveraging hidden states for implicit CON reasoning.
4 Related Work
Prior work shows that context relevance affects retrieval-augmented QA performance, while models may sometimes manage irrelevant contexts intrinsically. Related approaches therefore include training RALMs to disregard irrelevant contexts and broader Chain-of-X methods.
- Context relevance can substantially affect language-model QA performance, with random or irrelevant contexts sometimes harming results.
- Irrelevant context can sometimes improve performance when added to exemplars or task-specific instructions, suggesting models may manage such contexts intrinsically.
- Yoran et al. trained RALMs to disregard irrelevant contexts, a distinct approach that underscores context relevance in RALM effectiveness.
- The paper situates its approach among Chain-of-X methods, including Chain-of-Thought, with additional related approaches discussed in Appendix A.1.1 and A.2.
5 Conclusion
The paper introduces Chain-of-Note (CON), which generates sequential reading notes for retrieved documents to assess relevance and synthesize information for answering. Across four open-domain QA benchmarks, CON-equipped RALMs outperform standard fine-tuned RALMs.
- CON generates sequential reading notes for each retrieved document to assess relevance and synthesize information into a final answer.
- Across four open-domain QA benchmarks, CON-equipped RALMs significantly outperform standard fine-tuned RALMs.
- GPT-4 generated 10K CON training examples that were subsequently used to train a LLaMa-2 7B model.
6 Limitations
CON has higher inference cost because it generates notes sequentially, increasing response times and creating efficiency sensitivity to note quality and document complexity.
- CON’s sequential note generation increases inference cost and produces longer response times.
- The approach is problematic for time-sensitive applications because its longer response times reduce efficiency.
- System efficiency depends on the conciseness and relevance of generated notes, which can fluctuate with retrieved-document complexity.
A.1.1 Retrieval-Augmented Language Models
RALMs retrieve documents from a large evidence corpus and use a reader to examine them before formulating an answer.
- RALMs use a retriever to scan a large evidence corpus, such as Wikipedia, for documents pertinent to the user’s query.
- A reader component then examines the retrieved documents before formulating the output answer.
A.2 Chain-of-X Approaches in Large Language Models
Chain-of-Thought decomposes complex problems into intermediate steps, and related chain-of-X methods extend this staged reasoning pattern to diverse LLM challenges.
- Chain-of-Thought: Chain-of-Thought breaks complex issues into smaller components that receive focused attention during problem solving.The approach mirrors human problem-solving by organizing reasoning as a sequence of intermediate steps.
- Applications: Chain-of-Thought has been applied to multi-modal reasoning, multilingual scenarios, and knowledge-driven applications.
- Chain-of-X Approaches: Chain-of-X methods address diverse LLM application challenges beyond the original Chain-of-Thought formulation.The passage identifies chain-of-explanation, chain-of-knowledge, and chain-of-verification among these approaches.
A.3 Dataset Information
The experiments use four open-domain QA benchmarks and compare standard RALM, RALM with CoT, and RALM with CoN under retrieval and case-study settings. CoN is evaluated through a multi-step instruction that reads passages, writes notes, assesses relevance, and answers using relevant evidence or intrinsic knowledge.
- Dataset Information: TriviaQA contains trivia and quiz-league questions, while WebQ uses Google Suggest questions whose answers are Freebase entities.
- Dataset Information: NQ consists of real Google search queries answered by one or more human-annotated spans in Wikipedia articles.
- Implementation: DPR retrieves documents from Wikipedia, after which LLaMA-2 is fine-tuned for three epochs using batch size 128.
- Compared Systems: The compared systems are standard RALM, RALM with CoT, and RALM with CoN, with CoN instructed to read five passages, write notes, assess relevance, and answer conditionally.
- Case Studies: Case studies report that CoN answers the Olympics question correctly by distinguishing a bid from a successful event and answers the Jnanpith question by combining details across documents.