Papers by Potsawee Manakul
AudioJudge: Understanding What Works in Large Audio Model Based Speech Evaluation (2026.eacl-long)
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Potsawee Manakul, Woody Haosheng Gan, Michael J Ryan, Ali Sartaz Khan, Warit Sirichotedumrong, Kunat Pipatanakul, William Barr Held, Diyi Yang
| Challenge: | Current speech evaluation systems rely on specialized systems for individual audio characteristics and poor correlation between automatic methods and human preferences. |
| Approach: | They propose a unified evaluation framework for Large Audio Models as a Judge, AudioJudge . they propose specialized judges that can be prompted to perform audio characteristic detection tasks . |
| Outcome: | The proposed method improves performance across audio characteristic detection and human preference simulation tasks. |
Towards Better Understanding of Program-of-Thought Reasoning in Cross-Lingual and Multilingual Environments (2025.findings-acl)
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Patomporn Payoungkhamdee, Pume Tuchinda, Jinheon Baek, Samuel Cahyawijaya, Can Udomcharoenchaikit, Potsawee Manakul, Peerat Limkonchotiwat, Ekapol Chuangsuwanich, Sarana Nutanong
| Challenge: | Multi-step reasoning is essential for large language models, yet multilingual performance remains challenging. |
| Approach: | They propose a framework to evaluate Program-of-Thought (PoT) prompting by separating multilingual reasoning from code execution to examine impact of fine-tuning on question-reasoning alignment and reasoning quality. |
| Outcome: | The proposed framework outperforms CoT fine-tuned models in multilingual settings and shows strong correlation between reasoning quality and answer accuracy. |
LLM Comparative Assessment: Zero-shot NLG Evaluation through Pairwise Comparisons using Large Language Models (2024.eacl-long)
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| Challenge: | Recent advances in large language models have enabled impressive zero-shot capabilities across various natural language tasks. |
| Approach: | They propose two ways to exploit the emergent abilities of large language models for NLG assessment. |
| Outcome: | The proposed methods improve performance and positional biases in comparisons between candidates. |
Prior Prompt Engineering for Reinforcement Fine-Tuning (2025.emnlp-main)
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| Challenge: | Existing studies have focused on algorithms, reward shaping, and data curation, but prior prompt engineering is understudied. |
| Approach: | They investigate prior prompt engineering (pPE) in reinforcement fine-tuning . they translate five representative iPE strategies into corresponding pPE approaches . |
| Outcome: | The proposed approaches outperform iPE-prompted models on in-domain and out-of-domain benchmarks. |
McCrolin: Multi-consistency Cross-lingual Training for Retrieval Question Answering (2024.findings-emnlp)
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Peerat Limkonchotiwat, Wuttikorn Ponwitayarat, Lalita Lowphansirikul, Potsawee Manakul, Can Udomcharoenchaikit, Ekapol Chuangsuwanich, Sarana Nutanong
| Challenge: | Existing approaches struggle with consistency across multiple languages and multi-size input scenarios. |
| Approach: | They propose a cross-lingual training framework that leverages multi-task learning to enhance cross-linguistic consistency and ranking stability. |
| Outcome: | The proposed training framework outperforms competitors on various input sizes and architectures. |
An Empirical Study of Multilingual Reasoning Distillation for Question Answering (2024.emnlp-main)
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Patomporn Payoungkhamdee, Peerat Limkonchotiwat, Jinheon Baek, Potsawee Manakul, Can Udomcharoenchaikit, Ekapol Chuangsuwanich, Sarana Nutanong
| Challenge: | Existing efforts to distill reasoning capabilities have focused mainly on English, leaving multilingual distillation underexplored. |
| Approach: | They propose a method that incorporates incorrect rationales as additional guidance to improve multilingual reasoning in large language models. |
| Outcome: | Empirical results show that d-CoT-nR significantly surpasses the baseline, improving accuracy in unseen languages and correctness in step-by-step reasoning. |
Unlearning vs. Obfuscation: Are We Truly Removing Knowledge? (2025.emnlp-main)
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| Challenge: | Recent methods often rely on obfuscation by injecting incorrect or irrelevant information to suppress knowledge, leaving models vulnerable to probing. |
| Approach: | They propose a method that flattens the model predictive distribution over automatically generated multiple-choice questions, effectively removing knowledge about target individuals. |
| Outcome: | The proposed method achieves unlearning with over 90% refusal rate and a higher uncertainty than obfuscation on probing questions. |
Mind the Gap: Static and Interactive Evaluations of Large Audio Models (2025.acl-long)
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Minzhi Li, William Barr Held, Michael J Ryan, Kunat Pipatanakul, Potsawee Manakul, Hao Zhu, Diyi Yang
| Challenge: | Recent work has focused on evaluating large audio models (LAMs) that directly accept audio inputs. |
| Approach: | They propose an interactive approach to evaluate large audio models and collect 7,500 LAM interactions from 484 participants. |
| Outcome: | The proposed model is based on a set of user-generated audio interfaces with 7,500 interactions from 484 participants. |
SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models (2023.emnlp-main)
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| Challenge: | Existing fact-checking approaches require access to external databases or external databases . a lack of external databases can undermine trust in large language models. |
| Approach: | They propose a sampling-based approach to fact-check black-box models without external databases. |
| Outcome: | The proposed approach can be used to fact-check black-box models without external databases . it can detect non-factual and factual sentences and rank passages in terms of factuality . |
Extending Audio Context for Long-Form Understanding in Large Audio-Language Models (2026.eacl-long)
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Yuatyong Chaichana, Pittawat Taveekitworachai, Warit Sirichotedumrong, Potsawee Manakul, Kunat Pipatanakul
| Challenge: | Prior work has introduced context-extension methods (e.g. YaRN) on unimodal LLMs, yet their application to LALMs remains unexplored. |
| Approach: | They propose a training-free, modality-decoupled extension method that modifies only audio token positions, leaving text positions intact to preserve the base LLM’s text capabilities. |
| Outcome: | The proposed method outperforms the original models across wide range of settings and provides significant performance improvement on long audio of unseen lengths. |
Efficient Overshadowed Entity Disambiguation by Mitigating Shortcut Learning (2024.emnlp-main)
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Panuthep Tasawong, Peerat Limkonchotiwat, Potsawee Manakul, Can Udomcharoenchaikit, Ekapol Chuangsuwanich, Sarana Nutanong
| Challenge: | Entity disambiguation (ED) is crucial in natural language processing tasks such as question-answering and information extraction. |
| Approach: | They propose a method to reduce computational overhead on overshadowed entities by addressing shortcut learning. |
| Outcome: | The proposed method achieves state-of-the-art performance without compromising inference speed. |
Sparsity and Sentence Structure in Encoder-Decoder Attention of Summarization Systems (2021.emnlp-main)
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| Challenge: | Training and inference using large transformer models can be computationally expensive because the self-attention's time and memory grow quadratically with sequence length. |
| Approach: | They propose a modified transformer architecture that constrains the encoder-decoder attention mechanism to a subset of input sentences while maintaining system performance. |
| Outcome: | The proposed architecture can be trained and inferenced using large transformer models with expensive training and induction costs. |
SkillAggregation: Reference-free LLM-Dependent Aggregation (2025.acl-long)
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| Challenge: | Existing methods in NLP assign equal weight to all LLM judgments or are designed for specific tasks such as hallucination detection. |
| Approach: | They propose a method that learns to combine LLM judgments without additional data or ground truth to exploit the judge estimates during inference. |
| Outcome: | The proposed method outperforms Crowdlayer on all tasks and yields the best performance over all approaches on the majority of tasks. |
Long-Span Summarization via Local Attention and Content Selection (2021.acl-long)
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| Challenge: | Transformer-based models are state-of-the-art for a wide range of natural language processing tasks, including document summarization. |
| Approach: | They exploit large pre-trained transformer-based models and address long-span dependencies in abstractive summarization using two methods: local self-attention; and explicit content selection. |
| Outcome: | The proposed models achieve state-of-the-art on Spotify Podcast, arXiv, and PubMed datasets. |