Papers by Faisal Ladhak
Aligning Large Language Models via Fine-grained Supervision (2024.acl-short)
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| Challenge: | Pre-trained large-scale language models often generate biased or toxic text, misaligning with human intentions. |
| Approach: | They propose to use human feedback to improve LLM alignment by fine-grained token supervision . they ask annotators to edit less preferred responses to make them more favorable . |
| Outcome: | The proposed method improves LLM alignment by up to 5.1% in terms of win rate compared with the traditional model. |
Faithful or Extractive? On Mitigating the Faithfulness-Abstractiveness Trade-off in Abstractive Summarization (2022.acl-long)
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| Challenge: | Abstractive summarization systems still suffer from faithfulness errors, authors say . prior work has proposed models that improve faithfulness, but it is unclear whether this improvement comes from an increased level of extractiveness of the outputs. |
| Approach: | They propose a faithfulness-abstractiveness trade-off curve that serves as a control . they also learn a selector to identify the most faithful and abstractive summary for a given document . |
| Outcome: | The proposed model achieves higher faithfulness scores while being abstractive than the baseline system on two datasets. |
SWAN: An Efficient and Scalable Approach for Long-Context Language Modeling (2025.emnlp-main)
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Krishna C Puvvada, Faisal Ladhak, Santiago Akle Serano, Cheng-Ping Hsieh, Shantanu Acharya, Somshubra Majumdar, Fei Jia, Samuel Kriman, Simeng Sun, Dima Rekesh, Boris Ginsburg
| Challenge: | Existing decoder-only models struggle with context lengths beyond their training distribution. |
| Approach: | They propose a causal Transformer architecture that generalizes robustly to sequence lengths longer than seen during training. |
| Outcome: | The proposed decoder-only architecture can generalize robustly to longer contexts . it is more computationally efficient than the standard Transformer architecture, the authors say . |
When Do Pre-Training Biases Propagate to Downstream Tasks? A Case Study in Text Summarization (2023.eacl-main)
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Faisal Ladhak, Esin Durmus, Mirac Suzgun, Tianyi Zhang, Dan Jurafsky, Kathleen McKeown, Tatsunori Hashimoto
| Challenge: | Existing studies have shown that large language models contain linguistic and societal biases, but it is unclear how these biase amplify to downstream tasks. |
| Approach: | They investigate how name-nationality bias propagates from pre-training to downstream tasks . they show that these biases manifest themselves as hallucinations in summarization . |
| Outcome: | The proposed model can reduce the rate of hallucinations, but does not change the types of biases that do appear. |
Improving Faithfulness by Augmenting Negative Summaries from Fake Documents (2022.emnlp-main)
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| Challenge: | Current abstractive summarization systems tend to hallucinate unfaithful content . however, the most common method does not disentangle factual errors from other errors. |
| Approach: | They propose a back-translation-style approach to augment negative samples that mimic factual errors made by the model. |
| Outcome: | The proposed method improves faithfulness without sacrificing informativeness . it incorporates negative samples into training, and produces faithful/unfaithful summaries . |
STORYSUMM: Evaluating Faithfulness in Story Summarization (2024.emnlp-main)
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| Challenge: | Existing methods for evaluating abstractive summarization are lacking in faithfulness evaluation. |
| Approach: | They propose a dataset that measures faithfulness of LLM summaries with localized errors and faithfulness labels for evaluation methods. |
| Outcome: | The proposed method does not achieve more than 70% accuracy on this task. |
Spurious Correlations in Reference-Free Evaluation of Text Generation (2022.acl-long)
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| Challenge: | Recent work suggests that reference-free evaluation metrics may rely on spurious correlations with human judgments. |
| Approach: | They propose to use model-based, reference-free evaluation metrics to evaluate natural language generation systems. |
| Outcome: | The proposed metrics achieve high correlations with human judgments, but they may not be robust enough to evaluate their efficacy and robustness. |
Determining Relative Argument Specificity and Stance for Complex Argumentative Structures (P19-1)
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| Challenge: | Existing work on claim specificity and stance has been limited to shallow arguments . a system that can determine the stance of claims employed in argumentation is not sufficient . |
| Approach: | They propose to use a dataset of manually curated argument trees to study claim specificity and stance in argumentation. |
| Outcome: | The proposed dataset consists of manually curated argument trees for 741 controversial topics covering 95,312 unique claims. |
Contrastive Error Attribution for Finetuned Language Models (2023.acl-long)
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| Challenge: | Existing methods for error tracing do not detect faithfulness errors in NLG datasets. |
| Approach: | They propose a framework to identify and remove low-quality training instances that lead to undesirable outputs. |
| Outcome: | The proposed method outperforms existing methods for detecting faithfulness errors in NLG datasets. |
Segmenting Subtitles for Correcting ASR Segmentation Errors (2021.eacl-main)
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David Wan, Chris Kedzie, Faisal Ladhak, Elsbeth Turcan, Petra Galuscakova, Elena Zotkina, Zhengping Jiang, Peter Bell, Kathleen McKeown
| Challenge: | Typical ASR systems segment input audio into utterances using purely acoustic information, which may not resemble sentence-like units expected by conventional machine translation systems for spoken language translation (SLT). |
| Approach: | They propose a model for correcting ASR acoustic segmentation using subtitles as a proxy dataset for creating synthetic aural utterances by modeling common error modes. |
| Outcome: | The proposed model improves performance on MT and audio-document cross-language information retrieval (CLIR) it uses subtitles as a proxy dataset to correct ASR acoustic segmentation . |
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code (2022.emnlp-demos)
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Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran, Alex Wang, Alexandros Papangelis, Aman Madaan, Angelina Mcmillan-major, Anna Shvets, Ashish Upadhyay, Bernd Bohnet, Bingsheng Yao, Bryan Wilie, Chandra Bhagavatula, Chaobin You, Craig Thomson, Cristina Garbacea, Dakuo Wang, Daniel Deutsch, Deyi Xiong, Di Jin, Dimitra Gkatzia, Dragomir Radev, Elizabeth Clark, Esin Durmus, Faisal Ladhak, Filip Ginter, Genta Indra Winata, Hendrik Strobelt, Hiroaki Hayashi, Jekaterina Novikova, Jenna Kanerva, Jenny Chim, Jiawei Zhou, Jordan Clive, Joshua Maynez, João Sedoc, Juraj Juraska, Kaustubh Dhole, Khyathi Raghavi Chandu, Laura Perez Beltrachini, Leonardo F . R. Ribeiro, Lewis Tunstall, Li Zhang, Mahim Pushkarna, Mathias Creutz, Michael White, Mihir Sanjay Kale, Moussa Kamal Eddine, Nico Daheim, Nishant Subramani, Ondrej Dusek, Paul Pu Liang, Pawan Sasanka Ammanamanchi, Qi Zhu, Ratish Puduppully, Reno Kriz, Rifat Shahriyar, Ronald Cardenas, Saad Mahamood, Salomey Osei, Samuel Cahyawijaya, Sanja Štajner, Sebastien Montella, Shailza Jolly, Simon Mille, Tahmid Hasan, Tianhao Shen, Tosin Adewumi, Vikas Raunak, Vipul Raheja, Vitaly Nikolaev, Vivian Tsai, Yacine Jernite, Ying Xu, Yisi Sang, Yixin Liu, Yufang Hou
| Challenge: | Evaluations in machine learning rarely use the latest metrics, datasets, or human evaluation in favor of remaining compatible with prior work. |
| Approach: | They propose to use the Generation, Evaluation, and Metrics Benchmark to integrate new evaluation methods into existing evaluations. |
| Outcome: | The proposed evaluation infrastructure bridges the gap between the advantages of leaderboards and in-depth and evolving evaluations by allowing model developers to benefit from each other's work. |
Generating EDU Extracts for Plan-Guided Summary Re-Ranking (2023.acl-long)
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| Challenge: | Existing methods to generate summary candidates for re-ranking produce redundant, and often low quality, content. |
| Approach: | They propose a method to generate candidates for re-ranking that addresses these issues by grounding each abstract on its own unique content plan and creating distinct plan-guided abstracts using a model's top beam. |
| Outcome: | The proposed method outperforms baseline decoding methods on CNN, NYT, and Xsum and shows that prompting GPT-3 to follow EDU plans outperformed sampling-based methods by 1.05 points. |
Exploring Content Selection in Summarization of Novel Chapters (2020.acl-main)
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| Challenge: | We focus on extractive summarization, which requires the creation of a gold-standard set of extractive summary summaries. |
| Approach: | They propose a new metric for aligning summary sentences with chapter sentences to create gold extracts. |
| Outcome: | The proposed method improves on previous methods and automatic metrics and a crowd-sourced pyramid analysis. |
The Role of Pragmatic and Discourse Context in Determining Argument Impact (D19-1)
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| Challenge: | Recent work shows that attributes of both the audience and communicator constitute important cues for determining argument strength. |
| Approach: | They propose to use a dataset to study the pragmatic and discourse context of argumentative claims to build predictive models that incorporate the pragmatic context of the argument. |
| Outcome: | The proposed models outperform models that rely on claim-specific linguistic features for predicting the perceived impact of individual claims within a particular line of argument. |
WikiLingua: A New Benchmark Dataset for Cross-Lingual Abstractive Summarization (2020.findings-emnlp)
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| Challenge: | a lack of high quality multilingual data for cross-lingual summarization is a costly endeavor since it requires humans to read, comprehend, condense, and paraphrase entire articles. |
| Approach: | They propose to use a large-scale, multilingual dataset to evaluate cross-lingual abstractive summarization systems. |
| Outcome: | The proposed method significantly outperforms baseline approaches while being more cost efficient during inference. |
To BERT or Not to BERT: Comparing Task-specific and Task-agnostic Semi-Supervised Approaches for Sequence Tagging (2020.emnlp-main)
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Kasturi Bhattacharjee, Miguel Ballesteros, Rishita Anubhai, Smaranda Muresan, Jie Ma, Faisal Ladhak, Yaser Al-Onaizan
| Challenge: | Using large amounts of unlabeled data to improve performance has become the foundation for many natural language processing tasks. |
| Approach: | They propose a task-specific semi-supervised approach that uses unlabeled data in a more task-agnostic manner. |
| Outcome: | The proposed approach achieves similar performance to BERT on a set of sequence tagging tasks with less financial and environmental impact. |
Constrained Regeneration for Cross-Lingual Query-Focused Extractive Summarization (2022.coling-1)
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Elsbeth Turcan, David Wan, Faisal Ladhak, Petra Galuscakova, Sukanta Sen, Svetlana Tchistiakova, Weijia Xu, Marine Carpuat, Kenneth Heafield, Douglas Oard, Kathleen McKeown
| Challenge: | Query-focused summarization of foreign-language documents can help a user understand whether a document is relevant to a query term. |
| Approach: | They propose to use machine translation and post-editing to improve human relevance judgments . they include a query term in a summary when its translation appears in the source document . |
| Outcome: | The proposed approach improves human relevance judgments by including a query term in a summary when its translation appears in the source document. |
Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference (2025.acl-long)
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Benjamin Warner, Antoine Chaffin, Benjamin Clavié, Orion Weller, Oskar Hallström, Said Taghadouini, Alexis Gallagher, Raja Biswas, Faisal Ladhak, Tom Aarsen, Griffin Thomas Adams, Jeremy Howard, Iacopo Poli
| Challenge: | Encoder-only transformer models such as BERT offer a great performance-size tradeoff for retrieval and classification tasks compared to larger decoder models. |
| Approach: | They introduce a new transformer model, ModernBERT, which brings modern model optimizations to encoder-only transformer models. |
| Outcome: | The proposed model improves on the BERT transformer model and is faster and more memory efficient than the older models. |
ToKen: Task Decomposition and Knowledge Infusion for Few-Shot Hate Speech Detection (2022.emnlp-main)
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Badr AlKhamissi, Faisal Ladhak, Srinivasan Iyer, Veselin Stoyanov, Zornitsa Kozareva, Xian Li, Pascale Fung, Lambert Mathias, Asli Celikyilmaz, Mona Diab
| Challenge: | Hate speech detection is complex and requires commonsense reasoning and social nuance . prior work has shown that even humans cannot achieve a high agreement on whether a post constitutes HS . |
| Approach: | They frame a few-shot learning task to decompose a hate speech detection task into its "constituent" parts. they show that infusing commonsense knowledge from reasoning datasets improves the performance even further. |
| Outcome: | The proposed method outperforms baseline methods in the 16-shot case. |