Papers with Large Language models
Embodied Executable Policy Learning with Language-based Scene Summarization (2024.naacl-long)
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| Challenge: | Existing Large Language models with text inputs lack the capability to evolve with non-expert interactions with environments. |
| Approach: | They propose a novel learning paradigm that generates robots’ executable actions in the form of text, derived solely from visual observations. |
| Outcome: | The proposed learning paradigm surpasses baselines and can adapt to the target tasks effectively. |
EvEntS ReaLM: Event Reasoning of Entity States via Language Models (2022.emnlp-main)
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| Challenge: | Existing approaches to model event implications fail to reason about the world, despite their knowledge of physical attributes. |
| Approach: | They propose to use a model prompting technique to prompt models of event implications by targeting their understanding of physical attributes. |
| Outcome: | The proposed model prompting technique is especially useful for unseen attributes or when only limited data is available. |
Preserving Privacy Through Dememorization: An Unlearning Technique For Mitigating Memorization Risks In Language Models (2023.emnlp-main)
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| Challenge: | Large Language models (LLMs) are trained on vast amounts of data, including sensitive information that poses a risk to personal privacy if exposed. |
| Approach: | They propose a novel unlearning approach that utilizes an efficient reinforcement learning feedback loop via proximal policy optimization to incentivize the LLMs to learn a paraphrasing policy to unlearn the pre-training data. |
| Outcome: | The proposed approach surpasses strong baselines and state-of-the-art methods in terms of its ability to generalize and strike a balance between privacy and LLM performance. |
Aligning Translation-Specific Understanding to General Understanding in Large Language Models (2024.emnlp-main)
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| Challenge: | Large Language models (LLMs) have remarkable abilities in understanding complex texts . however, understanding misalignment leads to LLMs mistakenly translating complex concepts . |
| Approach: | They propose a translation process that aligns the translation-specific understanding with the general understanding to improve translation quality and reduce translation literalness. |
| Outcome: | The proposed translation process improves translation quality and reduces translation literalness by -25% -51%. |
TOOL-ED: Enhancing Empathetic Response Generation with the Tool Calling Capability of LLM (2025.coling-main)
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| Challenge: | Empathetic conversation is a crucial characteristic in daily conversations between individuals. |
| Approach: | They propose an Emotional Knowledge Tool Calling framework which encapsulates commonsense knowledge bases as empathetic tools, enabling LLMs to integrate external knowledge flexibly. |
| Outcome: | The proposed framework can generate empathetic responses effectively on the TOOL-ED dataset. |
SQLForge: Synthesizing Reliable and Diverse Data to Enhance Text-to-SQL Reasoning in LLMs (2025.findings-acl)
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| Challenge: | Existing closed-source LLMs have a performance gap in text-to-SQL reasoning tasks. |
| Approach: | They propose a SQL-based approach to synthesize reliable data to enhance text-to-SQL reasoning in LLMs. |
| Outcome: | The proposed model achieves state-of-the-art accuracy on the widely recognized Spider and BIRD benchmarks, significantly narrowing the performance gap with closed-source methods. |
ZigZagKV: Dynamic KV Cache Compression for Long-context Modeling based on Layer Uncertainty (2025.coling-main)
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| Challenge: | Existing methods to accelerate inference of Large Language models (LLMs) are limited in their ability to retain key tokens as input length increases. |
| Approach: | They propose a method that leverages layer uncertainty to allocate budget size for each layer to reduce memory usage. |
| Outcome: | The proposed method reduces memory usage of the KV caches to only 20% when compared to full KV inference while achieving nearly lossless performance. |
Differentially Private Knowledge Distillation via Synthetic Text Generation (2024.findings-acl)
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| Challenge: | Large Language models (LLMs) are achieving state-of-the-art performance in many downstream tasks, but data privacy is a major challenge for practitioners. |
| Approach: | They propose a differentially private knowledge distillation algorithm that exploits the knowledge of a teacher LLM and a student's output distribution. |
| Outcome: | The proposed algorithm significantly improves the utility over baselines on the Big Patent dataset, with strong privacy parameters, =2. |
TABGEN-ICL: Residual-Aware In-Context Example Selection for Tabular Data Generation (2025.findings-acl)
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| Challenge: | Existing approaches to tabular data generation require fine-tuning, which is computationally expensive. |
| Approach: | They propose a new in-context learning framework to prompt a fixed LLM with in-constitut examples to enhance the in-text learning ability of LLMs for tabular data generation. |
| Outcome: | The proposed framework outperforms random selection strategies on five real-world tabular datasets and reduces error rate by 42.2% on fidelity metric. |
Towards Informative Few-Shot Prompt with Maximum Information Gain for In-Context Learning (2023.findings-emnlp)
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| Challenge: | Large Language models (LLMs) have the capability to engage In-context Learning (ICL) however, this particular learning paradigm suffers from high instability stemming from factors such as input distribution, order and prompt formats. |
| Approach: | They propose to quantify the information gain obtained in prediction after observing a given example candidate and to sample those with maximum IG. |
| Outcome: | The proposed method can yield an average relative improvement of 14.3% across six classification tasks using three LLMs. |
HypER: Literature-grounded Hypothesis Generation and Distillation with Provenance (2025.emnlp-main)
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| Challenge: | Existing approaches focus on retrieval augmentation and focus on the quality of the output . Existing methods focus on generating a highly specific declarative statement ignoring the underlying reasoning process behind ideation. |
| Approach: | They propose a large language model that generates evidence-based hypotheses using literature-guided reasoning and a multi-task setting. |
| Outcome: | The proposed model outperforms the base model and generates evidence-grounded hypotheses with high feasibility and impact as judged by human experts. |
AraEval: An Arabic Multi-Task Evaluation Suite for Large Language Models (2025.emnlp-main)
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Alhanoof Althnian, Norah A. Alzahrani, Shaykhah Z. Alsubaie, Eman Albilali, Ahmed Abdelali, Nouf M. Alotaibi, M Saiful Bari, Yazeed Alnumay, Abdulhamed Alothaimen, Maryam Saif, Shahad D. Alzaidi, Faisal Abdulrahman Mirza, Yousef Almushayqih, Mohammed Al Saleem, Ghadah Alabduljabbar, Abdulmohsen Al-Thubaity, Areeb Alowisheq, Nora Al-Twairesh
| Challenge: | AraEval is a suite of evaluation tasks designed to assess the advanced knowledge, reasoning, truthfulness, and instruction following capabilities of large language models. |
| Approach: | They propose to use AraEval to assess the advanced knowledge, reasoning, truthfulness, and instruction following capabilities of large language models in the Arabic context. |
| Outcome: | The evaluation suite covers a broad spectrum of domains, including science, history, religion, and literature. |