FlexiQA: Leveraging LLM’s Evaluation Capabilities for Flexible Knowledge Selection in Open-domain Question Answering (2024.findings-eacl)
Copied to clipboard
| Challenge: | Current methods for open-domain question answering lacks the hallucination and relevance of acquired knowledge to the given question. |
| Approach: | They propose a new pipeline that utilizes the diverse evaluation capabilities of large language models to select knowledge effectively and flexibly. |
| Outcome: | The proposed pipeline combines the strengths of both paradigms and overcomes their shortcomings. |
Similar Papers
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. |
Beyond Prompting: An Efficient Embedding Framework for Open-Domain Question Answering (2025.acl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) have recently pushed open-domain question answering (ODQA) to new heights. |
| Approach: | They propose an embedding-level framework that enhances both the retriever and the reader by reordering query representations via lightweight linear layers under an unsupervised contrastive learning objective. |
| Outcome: | The proposed framework outperforms baselines in accuracy and efficiency across three open-source LLMs, three retrieval methods, and four ODQA benchmarks. |
JudgeAgent: Beyond Static Benchmarks for Knowledge-Driven and Dynamic LLM Evaluation (2026.findings-acl)
Copied to clipboard
Zhichao Shi, Xuhui Jiang, Chengjin Xu, Cangli Yao, Shengjie Ma, Yinghan Shen, Zixuan Li, Jian Guo, Yuanzhuo Wang
| Challenge: | Current evaluation methods for large language models rely on static benchmarks . limited knowledge coverage and fixed difficulties hinder the targeted optimizations resulting in superficial evaluations of LLMs - a problem that has been addressed by JudgeAgent . |
| Approach: | They propose a knowledge-driven and dynamic evaluation framework for large language models . judgeAgent leverages LLM agents equipped with context graphs to traverse knowledge structures . |
| Outcome: | The proposed framework can achieve comprehensive evaluations and facilitate effective model iterations. |
Generate then Select: Open-ended Visual Question Answering Guided by World Knowledge (2023.findings-acl)
Copied to clipboard
Xingyu Fu, Sheng Zhang, Gukyeong Kwon, Pramuditha Perera, Henghui Zhu, Yuhao Zhang, Alexander Hanbo Li, William Yang Wang, Zhiguo Wang, Vittorio Castelli, Patrick Ng, Dan Roth, Bing Xiang
| Challenge: | Open-ended Visual Question Answering (VQA) requires models to reason over visual and natural language inputs using world knowledge. |
| Approach: | They propose a new VQA pipeline that deploys a generate-then-select strategy guided by world knowledge for the first time. |
| Outcome: | The proposed pipeline expands the knowledge coverage from in-domain training data by 4.1% on OK-VQA, without additional computation cost. |
FastFiD: Improve Inference Efficiency of Open Domain Question Answering via Sentence Selection (2024.acl-long)
Copied to clipboard
| Challenge: | Open Domain Question Answering (ODQA) is a longstanding task in Natural Language Processing that involves generating an answer solely based on a given question. |
| Approach: | They propose a novel approach that executes sentence selection on the encoded passages to enhance the inference speed while reducing the context length required for generating answers. |
| Outcome: | The proposed approach can increase inference speed by **2.3X-5.7X** while maintaining the model’s performance. |
LLM-as-an-Interviewer: Beyond Static Testing Through Dynamic LLM Evaluation (2025.findings-acl)
Copied to clipboard
| Challenge: | Recent work on LLM-as-a-Judge has reported higher correlations with human judgments due to its static nature. |
| Approach: | They propose a framework that leverages multi-turn interactions where the LLM interviewer actively provides feedback on responses and poses follow-up questions to the evaluated LLM. |
| Outcome: | The proposed framework evaluates six models on reasoning, factuality and instruction-following tasks. |
Gradually Excavating External Knowledge for Implicit Complex Question Answering (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models (LLMs) have gained attention for their human-comparable capabilities but they may not solve open-domain implicit questions due to out-of-date domain knowledge, one-shot generation and restricted comprehensiveness. |
| Approach: | They propose a gradual knowledge excavation framework for open-domain complex question answering using extrinsic knowledge and historical knowledge. |
| Outcome: | The proposed framework achieves 78.17% accuracy with less than 6% parameters of its competitors, setting new SOTA in the 10B LLM class. |
CuriousLLM: Elevating Multi-Document Question Answering with LLM-Enhanced Knowledge Graph Reasoning (2025.naacl-industry)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have achieved significant success in open-domain question answering, however, they continue to face challenges such as knowledge cutoffs and hallucinations. |
| Approach: | They propose a new mechanism that integrates a curiosity-driven reasoning mechanism into an LLM agent to generate relevant follow-up questions. |
| Outcome: | The proposed enhancement integrates a curiosity-driven reasoning mechanism into an LLM agent, enabling it to generate relevant follow-up questions, thereby guiding the information retrieval process more efficiently. |
SnapNTell: Enhancing Entity-Centric Visual Question Answering with Retrieval Augmented Multimodal LLM (2024.findings-emnlp)
Copied to clipboard
Jielin Qiu, Andrea Madotto, Zhaojiang Lin, Paul Crook, Yifan Xu, Babak Damavandi, Xin Dong, Christos Faloutsos, Lei Li, Seungwhan Moon
| Challenge: | Vision-extended LLMs have made significant strides in VQA, but they still encounter significant difficulties in handling queries involving long-tail entities. |
| Approach: | They propose a benchmark to test models' ability to identify entities and provide detailed, entity-specific knowledge by combining 10 images and 10 knowledge-intensive QA pairs. |
| Outcome: | The proposed model outperforms existing methods on the SnapNTell dataset, achieving a 66.5% improvement in the BELURT score. |
Exploring Language Model Generalization in Low-Resource Extractive QA (2025.coling-main)
Copied to clipboard
| Challenge: | Existing LLMs struggle with dataset demands of closed domains such as medicine and law . current LLM performance in closed domain is lacking, even on traditional tasks such as Natural Language Inference . |
| Approach: | They investigate Extractive Question Answering (EQA) with Large Language Models (LLMs) under domain drift . they find that LLMs struggle with dataset demands of closed domains . |
| Outcome: | The proposed model performs poorly in extractive question answering tasks under domain drift . the proposed model can generalize to domains that require specific knowledge without training . |