Papers with LLaMA-3.1-8B
Nuanced Toxicity Detection in Spanish: A New Corpus and Benchmark Study (2026.findings-eacl)
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Alba María Mármol-Romero, Robiert Sepúlveda-Torres, Estela Saquete, María-Teresa Martín-Valdivia, L. Alfonso Ureña
| Challenge: | Existing corpora for Spanish are under-resourced for toxic content detection . sarcasm, indirect aggression, irony, and other toxicity are not detected in English . |
| Approach: | They propose to extend the NECOS-TOX corpus to include 4,011 Spanish comments . each comment is annotated across three levels of toxicity, with substantial inter-annotator agreement . |
| Outcome: | The proposed model performs on par with larger models and is released publicly . the proposed model is based on a human-in-the-loop active learning strategy . |
ToolFlow: Boosting LLM Tool-Calling Through Natural and Coherent Dialogue Synthesis (2025.naacl-long)
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Zezhong Wang, Xingshan Zeng, Weiwen Liu, Liangyou Li, Yasheng Wang, Lifeng Shang, Xin Jiang, Qun Liu, Kam-Fai Wong
| Challenge: | Large Language Models (LLMs) can be enhanced by using supervised fine-tuning . however, access to fine-timing data can be limited. |
| Approach: | They propose a Graph-based Sampling strategy and a Planned-generation strategy to enhance the coherence between dialogues by using 8,000 synthetic dialogues. |
| Outcome: | The proposed model achieves tool-calling performance comparable to or surpassing GPT-4 while maintaining strong general capabilities. |
MedCOD: Enhancing English-to-Spanish Medical Translation of Large Language Models Using Enriched Chain-of-Dictionary Framework (2025.findings-emnlp)
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| Challenge: | MedCOD integrates domain-specific structured knowledge into large language models . evaluators evaluated four open-source LLMs with structured prompts . |
| Approach: | They propose a framework that integrates domain-specific structured knowledge into large language models . they constructed a parallel corpus of 2,999 English-Spanish MedlinePlus articles . |
| Outcome: | The proposed framework improves translation quality across four open-source LLMs. |
WebAgent-R1: Training Web Agents via End-to-End Multi-Turn Reinforcement Learning (2025.emnlp-main)
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Zhepei Wei, Wenlin Yao, Yao Liu, Weizhi Zhang, Qin Lu, Liang Qiu, Changlong Yu, Puyang Xu, Chao Zhang, Bing Yin, Hyokun Yun, Lihong Li
| Challenge: | Existing work on reinforcement learning has focused on single-turn tasks such as solving math problems. |
| Approach: | They propose a framework that learns directly from online interactions by asynchronously generating diverse trajectories, guided by binary rewards depending on task success. |
| Outcome: | Experiments on the WebArena-Lite benchmark show that the framework outperforms state-of-the-art methods and strong proprietary models. |
UR2 : Unify RAG and Reasoning through Reinforcement Learning (2026.acl-long)
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| Challenge: | Existing attempts to unify large language models are limited to open-domain QA with fixed retrieval settings. |
| Approach: | They propose a general reinforcement learning framework that dynamically coordinates retrieval and reasoning. |
| Outcome: | The proposed framework outperforms existing paradigms on open-domain QA, MMLU-Pro, medical, and mathematical reasoning tasks. |
Chain of Strategy Optimization Makes Large Language Models Better Emotional Supporter (2025.findings-emnlp)
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Weixiang Zhao, Xingyu Sui, Xinyang Han, Yang Deng, Yulin Hu, Jiahe Guo, Libo Qin, Qianyun Du, Shijin Wang, Yanyan Zhao, Bing Qin, Ting Liu
| Challenge: | Existing supervised fine-tuning (SFT) fails to address these issues, as it trains models on single gold-standard responses without modeling nuanced strategy trade-offs. |
| Approach: | They propose a two-stage framework that optimizes strategy selection preferences at each dialogue turn. |
| Outcome: | The proposed framework improves strategy selection preferences at each dialogue turn. |
CARVQ: Corrective Adaptor with Group Residual Vector Quantization for LLM Embedding Compression (2025.findings-emnlp)
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Dayin Gou, Sanghyun Byun, Nilesh Malpeddi, Gabrielle De Micheli, Prathamesh Vaste, Jacob Song, Woo Seong Chung
| Challenge: | Large Language Models typically rely on a large number of parameters for token embedding, leading to substantial storage requirements and memory footprints. |
| Approach: | They propose a corrective Adaptor with group Residual Vector Quantization that can be used to compress the embedding layer without requiring specialized hardware. |
| Outcome: | The proposed corrective adaptor can achieve lower average bitwidth-per-parameter while maintaining reasonable perplexity and accuracy compared to scalar quantization. |
UniRAG: A Unified RAG Framework for Knowledge-Intensive Queries with Decomposition, Break-Down Reasoning, and Iterative Rewriting (2025.findings-emnlp)
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| Challenge: | Existing retrieval-augmented generation approaches struggle with query complexity, propagated reasoning errors, or rely on incomplete or noisy retrieval. |
| Approach: | a unified retrieval-augmented generation framework is developed to address query complexity . the framework decomposes queries into semantically coherent sub-queries . it explicitly verifies retrieved sub-facts and adaptively refines queries based on identified knowledge gaps. |
| Outcome: | a new framework improves answer completeness and reliability by decomposing queries into coherent sub-queries . the framework explicitly verifies retrieved sub-facts and adaptively refines queries based on identified knowledge gaps. |
Diverse, not Short: A Length-Controlled Data Selection Strategy for Improving Response Diversity of Language Models (2025.emnlp-main)
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| Challenge: | Diverse language model responses are crucial for creative generation, open-ended tasks, and self-improvement training. |
| Approach: | They propose a length-controlled data selection strategy that improves diversity while maintaining length parity. |
| Outcome: | The proposed method improves diversity while maintaining length parity on LLaMA-3.1-8B and Olmo-2 family. |