Papers by Silin Gao
Efficient Tool Use with Chain-of-Abstraction Reasoning (2025.coling-main)
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Silin Gao, Jane Dwivedi-Yu, Ping Yu, Xiaoqing Ellen Tan, Ramakanth Pasunuru, Olga Golovneva, Koustuv Sinha, Asli Celikyilmaz, Antoine Bosselut, Tianlu Wang
| Challenge: | Recent large language models have made progress at interpreting and executing instructions. |
| Approach: | They propose a method to decouple general reasoning from specialized knowledge . they propose to use abstract reasoning chains and domain tools to reify each chain . |
| Outcome: | The proposed method outperforms baseline methods on QA and mathematical reasoning domains. |
DiffuCOMET: Contextual Commonsense Knowledge Diffusion (2024.acl-long)
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| Challenge: | Recent methods for identifying contextually relevant commonsense inferences are weak . knowledge models are trained to verbalize tuples from general commonsens knowledge graphs . |
| Approach: | They develop a series of knowledge models that leverage diffusion to reconstruct semantic connections between narrative contexts and relevant commonsense knowledge. |
| Outcome: | The proposed model improves on two benchmarks, ComFact and WebNLG+, to measure commonsense diversity and contextual relevance. |
HyKnow: End-to-End Task-Oriented Dialog Modeling with Hybrid Knowledge Management (2021.findings-acl)
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| Challenge: | Task-oriented dialog systems typically manage structured knowledge to guide goal-oriented conversations. |
| Approach: | They propose a TOD system with hybrid knowledge management, HyKnow, which extends the belief state to manage both structured and unstructured knowledge. |
| Outcome: | The proposed model outperforms existing TOD systems in the evaluation of a multiWOZ dataset on unstructured knowledge with strong end-to-end performance. |
Paraphrase Augmented Task-Oriented Dialog Generation (2020.acl-main)
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| Challenge: | Neural generative models can perform dialog generation tasks with a large data set, but lack of high-quality data and expensive data annotation process limit their application in real world settings. |
| Approach: | They propose to combine paraphrase and response generation models to improve dialog generation performance by annotating dialog states and dialog act labels. |
| Outcome: | The proposed framework outperforms existing methods significantly in dialog generation tasks, especially under low resource settings. |
PACE: Prefix-Protected and Difficulty-Aware Compression for Efficient Reasoning (2026.findings-acl)
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Ruixiang Feng, Yuntao Wen, Silin Zhou, Ke Shi, Yifan Wang, Ran Le, Zhenwei An, Zongchao Chen, Chen Yang, Guangyue Peng, Yiming Jia, Dongsheng Wang, Tao Zhang, Lisi Chen, Yang Song, Shen Gao, Shuo Shang
| Challenge: | Existing LRMs often suffer from "overthinking" and excessively long reasoning traces . a dual-level framework for length compression of LRM is proposed . |
| Approach: | They propose a framework for prefix-protected and difficulty-aware compression under hierarchical supervision. |
| Outcome: | The proposed framework reduces token usage while improving accuracy on math benchmarks. |
ComFact: A Benchmark for Linking Contextual Commonsense Knowledge (2022.findings-emnlp)
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| Challenge: | Existing methods to retrieve facts from commonsense knowledge graphs are imprecise, requiring heuristics that ignore contexts and ambiguity . a novel benchmark, ComFact, contains 293k in-context relevance annotations for commonsensense triplets . |
| Approach: | They propose a task of commonsense fact linking where models are given contexts and trained to identify situationally-relevant commonsensical knowledge from KGs. |
| Outcome: | The proposed benchmark shows that heuristic fact linking approaches are imprecise . however, the models still significantly underperform humans in the commonsense augmentation task . |
Apertus: Democratizing Open and Compliant LLMs for Global Language Environments (2026.acl-long)
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Alejandro Hernández-Cano, Alexander Hägele, Allen Hao Huang, Angelika Romanou, Antoni-Joan Solergibert, Barna Pásztor, Bettina Messmer, Dhia Garbaya, Eduard Frank Ďurech, Ido Hakimi, Juan Garcia Giraldo, Mete Ismayilzada, Negar Foroutan, Skander Moalla, Tiancheng Chen, Vinko Sabolčec, Yixuan Xu, Michael Aerni, Badr AlKhamissi, Inés Altemir Marinas, Mohammad Hossein Amani, Matin Ansaripour, Ilia Badanin, Harold Benoit, Emanuela Boros, Nicholas John Browning, Fabian Bösch, Maximilian Böther, Niklas Canova, Camille Challier, Clément Charmillot, Jonathan Coles, Jan Milan Deriu, Arnout Devos, Lukas Drescher, Daniil Dzenhaliou, Maud Ehrmann, Dongyang Fan, Simin Fan, Silin Gao, Miguel Gila, María Grandury, Diba Hashemi, Alexander Miserlis Hoyle, Jiaming Jiang, Mark Klein, Andrei Kucharavy, Anastasiia Kucherenko, Frederike Lübeck, Roman Machacek, Theofilos Ioannis Manitaras, Andreas Marfurt, Kyle Matoba, Simon Matrenok, Henrique Mendonça, Fawzi Roberto Mohamed, Syrielle Montariol, Luca Mouchel, Sven Najem-Meyer, Jingwei Ni, Gennaro Oliva, Matteo Pagliardini, Elia Palme, Andrei Panferov, Léo Paoletti, Marco Passerini, Ivan Pavlov, Auguste Poiroux, Kaustubh Ponkshe, Nathan Ranchin, Javier Rando, Mathieu Sauser, Jakhongir Saydaliev, Mukhammadali Sayfiddinov, Marian Schneider, Stefano Schuppli, Marco Scialanga, Andrei Semenov, Kumar Shridhar, Raghav Singhal, Anna Sotnikova, Alexander Sternfeld, Ayush Kumar Tarun, Paul Teiletche, Jannis Vamvas, Xiaozhe Yao, Hao Zhao, Alexander Ilic, Ana Klimovic, Andreas Krause, Caglar Gulcehre, David Rosenthal, Elliott Ash, Florian Tramèr, Joost VandeVondele, Livio Veraldi, Martin Rajman, Thomas C. Schulthess, Torsten Hoefler, Antoine Bosselut, Martin Jaggi, Imanol Schlag
| Challenge: | Apertus is a fully open suite of large language models (LLMs) designed to address responsibility shortcomings in today’s open model ecosystem, namely data responsibility and global representation. |
| Approach: | They propose to release a fully open suite of large language models (LLMs) that address data responsibility and global representation shortcomings in today’s open model ecosystem. |
| Outcome: | The proposed model is pretrained on openly available data and suppresses verbatim recall of data while retaining task performance. |
PeaCoK: Persona Commonsense Knowledge for Consistent and Engaging Narratives (2023.acl-long)
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Silin Gao, Beatriz Borges, Soyoung Oh, Deniz Bayazit, Saya Kanno, Hiromi Wakaki, Yuki Mitsufuji, Antoine Bosselut
| Challenge: | a new knowledge graph for personas based on human-validated persona facts is constructed to model diverse persona attributes . a variety of persona characteristics are required to sustain coherent narratives . |
| Approach: | They construct a large-scale persona commonsense knowledge graph with 100K human-validated persona facts. |
| Outcome: | The proposed graph contains rich and precise world persona inferences that help systems generate more consistent and engaging narratives. |