How Credible Is an Answer From Retrieval-Augmented LLMs? Investigation and Evaluation With Multi-Hop QA (2025.coling-main)
Copied to clipboard
| Challenge: | Retrieval-augmented large language models (RaLLMs) are reshaping knowledge acquisition, offering long-form, knowledge-grounded answers through advanced reasoning and generation capabilities. |
| Approach: | They propose a benchmarking system to evaluate RaLLMs' correctness and Groundedness to determine their reliability in multi-hop question-answering tasks. |
| Outcome: | The proposed model-based evaluation pipeline for multi-hop question-answering tasks reveals that the model generates inaccuracies when dealing with flawed or partial knowledge. |
Similar Papers
RaLLe: A Framework for Developing and Evaluating Retrieval-Augmented Large Language Models (2023.emnlp-demo)
Copied to clipboard
Yasuto Hoshi, Daisuke Miyashita, Youyang Ng, Kento Tatsuno, Yasuhiro Morioka, Osamu Torii, Jun Deguchi
| Challenge: | Existing libraries for building R-LLMs provide high-level abstractions without sufficient transparency for evaluating and optimizing prompts within specific inference processes. |
| Approach: | They propose an open-source framework to facilitate the development, evaluation, and optimization of R-LLMs for knowledge-intensive tasks. |
| Outcome: | The framework improves hand-crafted prompts, inference processes and quantitatively measures overall system performance. |
Fact, Fetch, and Reason: A Unified Evaluation of Retrieval-Augmented Generation (2025.naacl-long)
Copied to clipboard
Satyapriya Krishna, Kalpesh Krishna, Anhad Mohananey, Steven Schwarcz, Adam Stambler, Shyam Upadhyay, Manaal Faruqui
| Challenge: | Recent advances in Large Language Models (LLMs) have significantly enhanced their capabilities across various cognitive tasks. |
| Approach: | They propose a high-quality evaluation dataset to test LLMs' ability to provide factual responses, assess retrieval capabilities, and evaluate the reasoning required to generate final answers. |
| Outcome: | The proposed framework improves performance in end-to-end RAG scenarios. |
DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text (2024.findings-naacl)
Copied to clipboard
| Challenge: | Retrievalaugmented LLMs have been used to ground LLM in external knowledge . a gap exists in the current landscape regarding the effectiveness of grounding LLM on heterogeneous knowledge sources. |
| Approach: | They propose a model that uses symbolic language to generate symbolic queries . they use a dataset that is generated using predefined reasoning chains and human annotation . |
| Outcome: | The proposed model outperforms previous approaches by a significant margin in QA tasks over text. |
MINTQA: A Multi-Hop Question Answering Benchmark for Evaluating LLMs on New and Long-tail Knowledge (2026.acl-long)
Copied to clipboard
| Challenge: | Existing studies have shown that large language models can handle knowledge with varying familiarity. |
| Approach: | They propose a benchmark to evaluate multi-hop question answering on new and tail knowledge . they use RAG to integrate external knowledge into large language models . |
| Outcome: | The proposed benchmark evaluates the multi-hop reasoning ability of large language models . it primarily evaluates their ability to handle knowledge with different levels of familiarity . |
SAGE: A Search-AuGmented Evaluation of Large Language Models on Free-Form QA (2026.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are prone to hallucination and rely on static, pre-annotated references for evaluation. |
| Approach: | They propose a framework to assess large language models without fixed ground-truth answers by iteratively generating web queries and synthesizing external evidence. |
| Outcome: | The proposed framework achieves substantial to perfect agreement with human evaluations on multiple free-form QA benchmarks. |
RAD-Bench: Evaluating Large Language Models’ Capabilities in Retrieval Augmented Dialogues (2025.naacl-industry)
Copied to clipboard
| Challenge: | Existing benchmarks assess LLMs' chat abilities in multi-turn dialogues or their use of retrieval for augmented responses in limited tasks such as knowledge QA or numeric reasoning. |
| Approach: | They propose a benchmark to evaluate LLMs' capabilities in multi-turn dialogues following retrievals. |
| Outcome: | The proposed benchmark evaluates LLMs' ability to perform in multi-turn dialogues following retrievals over 6 representative scenarios. |
Groundedness in Retrieval-augmented Long-form Generation: An Empirical Study (2024.findings-naacl)
Copied to clipboard
| Challenge: | a significant portion of correct answers remain compromised by hallucinations in large language models. |
| Approach: | They examine whether every generated sentence is grounded in retrieved documents or the model’s pre-training data. |
| Outcome: | The findings highlight the need for more robust mechanisms in large language models to mitigate the generation of ungrounded content. |
Rational Synthesizers or Heuristic Followers? Analyzing LLMs in RAG-based Question-Answering (2026.findings-acl)
Copied to clipboard
| Challenge: | Retrieval-Augmented Generation (RAG) is the prevailing paradigm for grounding Large Language Models. |
| Approach: | They propose a method to integrate conflicting retrieved evidence into large language models. |
| Outcome: | The proposed model is based on a curated dataset of 1,635 controversial questions paired with 15,058 diversely-sourced evidence documents. |
LLMs are Biased Evaluators But Not Biased for Fact-Centric Retrieval Augmented Generation (2025.findings-acl)
Copied to clipboard
| Challenge: | Recent studies have shown that large language models (LLMs) exhibit significant biases in evaluation tasks, especially in preferentially rating and favoring self-generated content. |
| Approach: | They propose to simulate two critical phases of retrieval-augmented generation (RAG) frameworks where keyword extraction and factual accuracy take precedence over stylistic elements. |
| Outcome: | The proposed model emulates two critical phases of the retrieval-augmented generation framework. |
RetroLLM: Empowering Large Language Models to Retrieve Fine-grained Evidence within Generation (2025.acl-long)
Copied to clipboard
| Challenge: | Existing methods rely on separate retrievers to fetch top-k text chunks for generating evidence, and they lack joint optimization. |
| Approach: | They propose a framework that integrates retrieval and generation into a single, auto-regressive process, enabling LLMs to directly generate fine-grained evidence from the corpus with constrained decoding. |
| Outcome: | Extensive experiments on five open-domain QA datasets demonstrate the proposed framework’s superior performance across both in-domain and out-of-domain tasks. |