Papers with knowledge and reasoning
Team SVMrank: Leveraging Feature-rich Support Vector Machines for Ranking Explanations to Elementary Science Questions (D19-53)
Copied to clipboard
| Challenge: | TextGraphs 2019 Shared Task on Multi-Hop Inference for Explanation Regeneration tackles explanation generation for elementary science questions. |
| Approach: | They propose a hybrid pipelined machine learning model and rule-based system to address MIER-19 . they use a featurerich learning-to-rank machine learning and a rule-driven system to rerank the LTR model predictions. |
| Outcome: | The proposed model was ranked fourth in the evaluation, close to the second and third ranked teams, achieving 39.4% MAP. |
User Feedback Alignment for LLM-powered Exploration in Large-scale Recommendation Systems (2025.acl-industry)
Copied to clipboard
Jianling Wang, Yifan Liu, Yinghao Sun, Xuejian Ma, Yueqi Wang, He Ma, Zhengyang Su, Minmin Chen, Mingyan Gao, Onkar Dalal, Ed H. Chi, Lichan Hong, Ningren Han, Haokai Lu
| Challenge: | Large Language Models (LLMs) can be used to broaden user experiences beyond established preferences and reinforce feedback loops. |
| Approach: | They propose a hierarchical approach that combines hierarchic planning with LLM inference-time scaling to improve recommendation relevancy without compromising novelty. |
| Outcome: | The proposed approach shows significant gains in both user satisfaction and exploration diversity. |
Reasoning Requirements for Indirect Speech Act Interpretation (2020.coling-main)
Copied to clipboard
| Challenge: | Existing systems that pretrain word and sentence embeddings to account for nearby linguistic context are unclear how to integrate extra-linguistic context into NLU. |
| Approach: | They perform a corpus analysis to develop a representation of the knowledge and reasoning used to interpret indirect speech acts. |
| Outcome: | The proposed model is based on the domain-general patterns of reasoning involved and implements Answer Set programming. |
GAOKAO-MM: A Chinese Human-Level Benchmark for Multimodal Models Evaluation (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing datasets focus on primary perception abilities and commonsense knowledge, or have low level of text comprehension difficulty, which are insufficient to reflect comprehensive capabilities of large vision-language models. |
| Approach: | They propose a multimodal benchmark based on the Chinese College Entrance Examination (GAOKAO) which sets human-level requirements for the model’s abilities, including perception, understanding, knowledge and reasoning. |
| Outcome: | The proposed model derives from native Chinese context and sets human-level requirements for its abilities, including perception, understanding, knowledge and reasoning. |
Diversification Catalyzes Language Models’ Instruction Generalization To Unseen Semantics (2025.findings-acl)
Copied to clipboard
| Challenge: | Instruction-tuned language models excel in knowledge, reasoning, and instruction-following . however, the factors enabling generalization to unseen instructions remain underexplored . |
| Approach: | They propose to model instruction-following as a computational process and design controlled experiments inspired by the Turing-complete Markov algorithm to disentangle its dynamics. |
| Outcome: | The proposed model outperforms scaling up data volumes in generalist models by combining in-domain and diverse out-of-domain tasks. |
Ro-SLM: Onboard Small Language Models for Robot Task Planning and Operation Code Generation (2026.findings-acl)
Copied to clipboard
| Challenge: | Recent advances in large language models (LLMs) provide robots with contextual reasoning abilities to comprehend human instructions. |
| Approach: | They propose a framework that enables reliable SLM-driven robot operation by distilling LLMs’ knowledge and reasoning. |
| Outcome: | The proposed framework enables reliable SLM-driven robot operation by distilling LLMs’ knowledge and reasoning. |