Papers with enhancement methods
The Reasoning Trap: How Enhancing LLM Reasoning Amplifies Tool Hallucination (2026.acl-long)
Copied to clipboard
| Challenge: | Recent studies suggest that strengthening reasoning often coincides with increased hallucination . however, no prior work has examined whether reasoning enhancement itself causes tool hallucinism . |
| Approach: | They propose a diagnostic benchmark measuring tool hallucination in two failure modes . they demonstrate a causal relationship between enhancing reasoning and tool hallubulation . |
| Outcome: | The proposed benchmark measures tool hallucination in two failure modes: no tool available, and (ii) only distractor tools available. |
CausalEval: Towards Better Causal Reasoning in Language Models (2025.naacl-long)
Copied to clipboard
Longxuan Yu, Delin Chen, Siheng Xiong, Qingyang Wu, Dawei Li, Zhikai Chen, Xiaoze Liu, Liangming Pan
| Challenge: | Large language models (LLMs) have been used for a variety of tasks, including problem-solving, decision-making, and understanding of the world. |
| Approach: | They propose a review of existing methods aimed at enhancing LMs for causal reasoning . they categorize existing methods as reasoning engines or as helpers providing knowledge or data to traditional methods . |
| Outcome: | The proposed methods perform better than existing methods on a range of tasks. |
Can LMs Generalize to Future Data? An Empirical Analysis on Text Summarization (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing summarization benchmarks overlap in time with pre-training corpora and fine-tuning datasets. |
| Approach: | They propose a temporal generalization benchmark that contains data samples from 2010 to 2022 to understand the temporal ability of abstractive summarization models. |
| Outcome: | The proposed benchmark analyzes data samples from 2010 to 2022 to understand the temporal generalization ability of abstractive summarization models. |
SegDRE: A Salient Entity Guided Approach to Document-Level Relation Extraction (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing models struggle to address two major bottlenecks in Document-level Relation Extraction: extreme class imbalance and complexity of multi-hop reasoning. |
| Approach: | They propose a method that decouples the extraction space into dense and sparse scenarios. |
| Outcome: | The proposed approach yields consistent improvements over various backbone models and achieves advanced performance compared to existing enhancement methods. |