Papers with question-answering benchmarks
ThinkNote: Enhancing Knowledge Integration and Utilization of Large Language Models via Constructivist Cognition Modeling (2026.findings-eacl)
Copied to clipboard
Zhipeng Xu, Zhenghao Liu, Yukun Yan, Shuo Wang, Shi Yu, Zheni Zeng, Chaojun Xiao, Zhiyuan Liu, Ge Yu, Chenyan Xiong
| Challenge: | Large Language Models (LLMs) exhibit suboptimal behaviors and inconsistencies when exposed to unfamiliar external information, underscoring their limitations in effectively leveraging such knowledge. |
| Approach: | They propose a framework that enhances the external knowledge utilization of Large Language Models through a two-stage constructivist cognitive modeling process. |
| Outcome: | The proposed framework achieves a 10% improvement over baseline methods on various question-answering benchmarks. |
Cooperative Self-training of Machine Reading Comprehension (2022.naacl-main)
Copied to clipboard
| Challenge: | Pretrained language models provide high-quality contextualized word embeddings, but training question answering models requires large amounts of annotated data for specific domains. |
| Approach: | They propose a framework for automatically generating more non-trivial question-answer pairs to improve model performance. |
| Outcome: | The proposed framework outperforms state-of-the-art (SOTA) pretrained language models and transfer learning approaches on standard question-answering benchmarks. |
MATHSENSEI: A Tool-Augmented Large Language Model for Mathematical Reasoning (2024.naacl-long)
Copied to clipboard
| Challenge: | TALMs have been successfully employed in question-answering benchmarks, but their efficacy on complex mathematical reasoning benchmarks are open research questions. |
| Approach: | They propose a tool-augmented large language model for mathematical reasoning that enhances the skillset of large language models (LLMs) by 13.5%. |
| Outcome: | The proposed model achieves better accuracy and better knowledge retrieval performance than existing tools. |
Tagging-Augmented Generation: Assisting Language Models in Finding Intricate Knowledge In Long Contexts (2025.emnlp-industry)
Copied to clipboard
Anwesan Pal, Karen Hovsepian, Tinghao Guo, Mengnan Zhao, Somendra Tripathi, Nikos Kanakaris, George Mihaila, Sumit Nigam
| Challenge: | Recent studies into effective context lengths of flagship large language models (LLMs) have revealed major limitations in effective question answering (QA) and reasoning over long and complex contexts for even the largest and most impressive cadre of models. |
| Approach: | They propose a lightweight data augmentation strategy that boosts LLM performance in long-context scenarios without degrading and altering the integrity and composition of retrieved documents. |
| Outcome: | The proposed strategy boosts performance in long-context scenarios without degrading and altering the integrity and composition of retrieved documents. |
DashboardQA: Benchmarking Multimodal Agents for Question Answering on Interactive Dashboards (2026.findings-eacl)
Copied to clipboard
Aaryaman Kartha, Ahmed Masry, Mohammed Saidul Islam, Thinh Lang, Shadikur Rahman, Ridwan Mahbub, Mizanur Rahman, Mahir Ahmed, Md Rizwan Parvez, Enamul Hoque, Shafiq Joty
| Challenge: | Existing question-answering benchmarks for data visualizations focus on static charts instead of interactive dashboards. |
| Approach: | They propose a benchmark to assess how vision-language GUI agents comprehend and interact with real-world dashboards. |
| Outcome: | The first benchmark explicitly designed to assess how vision-language GUI agents comprehend and interact with real-world dashboards. |
Don’t Generate, Classify! Low-Latency Prompt Optimization with Structured Complementary Prompt (2026.eacl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) have demonstrated strong performance across diverse tasks, but their performance varies significantly across different prompts. |
| Approach: | They propose a framework that reframes prompt engineering as a classification problem. |
| Outcome: | The proposed framework improves answer quality by up to 26.5% in win rate compared to prior methods while reducing latency by upto 1,956 times. |
Anchor-based Large Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) require massive GPU memory due to their size and parameter count. |
| Approach: | They propose to use anchor-based self-attention network and anchor-basic inference strategy to compress sequence information into an anchor token, reducing the keys/values cache and enhancing inference efficiency. |
| Outcome: | The proposed model reduces the key/value cache and improves inference efficiency by 99% while maintaining similar accuracy levels. |
Llama SLayer 8B: Shallow Layers Hold the Key to Knowledge Injection (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to augment pre-trained large language models require extensive computational efforts and massive data volumes, challenging the widespread accessibility of LLM research. |
| Approach: | They propose a post-pretraining strategy of selectively enhancing shallow layers while pruning less effective deep ones to augment pretrained large language models. |
| Outcome: | The proposed approach improves performance on the corpus of code & math and a legal corpus and is widely applicable. |
UOUO: Uncontextualized Uncommon Objects for Measuring Knowledge Horizons of Vision Language Models (2024.emnlp-main)
Copied to clipboard
Xinyu Pi, Mingyuan Wu, Jize Jiang, Haozhen Zheng, Beitong Tian, ChengXiang Zhai, Klara Nahrstedt, Zhiting Hu
| Challenge: | Vision-Language Models (VLMs) perform on par with larger models in general domain visual grounding and question-answering benchmarks. |
| Approach: | They propose a "Uncontextualized Uncommon Objects" benchmark to evaluate their performance on common datasets. |
| Outcome: | The proposed benchmark focuses on systematically testing VLMs with both large and small parameter counts on rare and specialized objects. |
XAutoLM: Efficient Fine-Tuning of Language Models via Meta-Learning and AutoML (2025.emnlp-main)
Copied to clipboard
Ernesto Luis Estevanell Valladares, Suilan Estevez-Velarde, Yoan Gutierrez, Andrés Montoyo, Ruslan Mitkov
| Challenge: | XAutoLM is a meta-learning-augmented framework that can be used to optimize discriminative and generative LM fine-tuning pipelines. |
| Approach: | They propose a meta-learning-augmented AutoML framework that reuses past experiences to optimize discriminative and generative LM fine-tuning pipelines efficiently. |
| Outcome: | XAutoLM surpasses zero-shot optimizer’s peak F1 on five of six tasks, reduces mean evaluation time of pipelines by up to 4.5x, and uncovers 50% more pipelines above zero- shot Pareto front. |
EpMAN: Episodic Memory AttentioN for Generalizing to Longer Contexts (2025.acl-long)
Copied to clipboard
Subhajit Chaudhury, Payel Das, Sarathkrishna Swaminathan, Georgios Kollias, Elliot Nelson, Khushbu Pahwa, Tejaswini Pedapati, Igor Melnyk, Matthew Riemer
| Challenge: | Recent advances in Large Language Models (LLMs) have yielded impressive successes on many language tasks, but efficient processing of long contexts remains a significant challenge. |
| Approach: | They propose a method for processing long contexts in an episodic memory module while holistically attending to semantically-relevant context chunks. |
| Outcome: | The proposed method outperforms baseline decoders on multiple long-context recall and question-answering benchmarks on 16k to 256k tokens. |
IntroLM: Introspective Language Models via Prefilling-Time Self-Evaluation (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to predict output quality of large language models rely on external classifiers with limited context windows and constrained representational capacity. |
| Approach: | They propose a method that enables causal language models to predict their own output quality during the prefilling phase without affecting generation using [CPX] tokens. |
| Outcome: | The proposed method outperforms existing classifiers on Qwen3-8B and DeBERTa-v3-Large models by 14% on question-answering benchmarks. |
Systematic Assessment of Factual Knowledge in Large Language Models (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing question-answering benchmarks for large language models have limitations regarding factual knowledge coverage, as they focus on generic domains and overlap with pretraining data. |
| Approach: | They propose a framework to assess the factual knowledge of large language models by leveraging knowledge graphs. |
| Outcome: | The proposed framework generates questions and expected answers from the facts stored in a given knowledge graph and evaluates them with KGs in generic and specific domains. |