Papers by Salman Avestimehr
Ethos: Rectifying Language Models in Orthogonal Parameter Space (2024.findings-naacl)
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| Challenge: | Language models (LMs) generate toxic, biased content and reveal private training records. |
| Approach: | They propose an efficient approach that rectifies LMs to mitigate toxicity and bias . Ethos distinguishes general beneficial and undesired knowledge when reconstructing task vectors . |
| Outcome: | The proposed approach mitigates toxicity and bias in outputs and avoids privacy leakage. |
ScaleLLM: A Resource-Frugal LLM Serving Framework by Optimizing End-to-End Efficiency (2024.emnlp-industry)
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Yuhang Yao, Han Jin, Alay Shah, Shanshan Han, Zijian Hu, Dimitris Stripelis, Yide Ran, Zhaozhuo Xu, Salman Avestimehr, Chaoyang He
| Challenge: | Large language models (LLMs) are widely used in commercial applications . low latency is crucial due to system latency, query concurrency, and computational resources constraints. |
| Approach: | They propose a system that can be resource-efficiently served by addressing bottlenecks beyond LLM inference . they propose 4.3 speed up over vLLM and 1.5 higher throughput . |
| Outcome: | The proposed system outperforms state-of-the-arts with 1.5 higher throughput . it achieves 4.3 speed up with 64 concurrent requests on Mixtral 8x7B . |
TensorOpera Router: A Multi-Model Router for Efficient LLM Inference (2024.emnlp-industry)
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Dimitris Stripelis, Zhaozhuo Xu, Zijian Hu, Alay Shah, Han Jin, Yuhang Yao, Jipeng Zhang, Tong Zhang, Salman Avestimehr, Chaoyang He
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable performance across a diverse set of domain-specific tasks. |
| Approach: | They propose a non-monolithic LLM querying system that seamlessly integrates various LLM experts into a single query interface and dynamically routes incoming queries to the most high-performant expert based on query’s requirements. |
| Outcome: | The proposed model improves query efficiency by 40% and costs by 30% while maintaining or enhancing model performance by 10%. |
Do Not Design, Learn: A Trainable Scoring Function for Uncertainty Estimation in Generative LLMs (2025.findings-naacl)
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Duygu Nur Yaldiz, Yavuz Faruk Bakman, Baturalp Buyukates, Chenyang Tao, Anil Ramakrishna, Dimitrios Dimitriadis, Jieyu Zhao, Salman Avestimehr
| Challenge: | Existing methods for probability-based UE are limited by their inability to handle biased probabilities and complex semantic dependencies between tokens. |
| Approach: | They propose a learning-based scoring function that captures complex dependencies between tokens and probabilities and produces more reliable responses. |
| Outcome: | The proposed function outperforms existing scoring functions in question-answering and arithmetical reasoning tasks with different datasets. |
TruthTorchLM: A Comprehensive Library for Predicting Truthfulness in LLM Outputs (2025.emnlp-demos)
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Duygu Nur Yaldiz, Yavuz Faruk Bakman, Sungmin Kang, Alperen Öziş, Hayrettin Eren Yildiz, Mitash Ashish Shah, Zhiqi Huang, Anoop Kumar, Alfy Samuel, Daben Liu, Sai Praneeth Karimireddy, Salman Avestimehr
| Challenge: | Generative Large Language Models (LLMs) produce untruthful outputs, referred to as hallucinations, which are often referred as false positives. |
| Approach: | They propose an open-source Python library with over 30 truthfulness prediction methods. |
| Outcome: | The proposed methods span diverse trade-offs in computational cost, access level, grounding document requirements, and supervision type (self-supervised or supervised). |
FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks (2022.findings-naacl)
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Bill Yuchen Lin, Chaoyang He, Zihang Ze, Hulin Wang, Yufen Hua, Christophe Dupuy, Rahul Gupta, Mahdi Soltanolkotabi, Xiang Ren, Salman Avestimehr
| Challenge: | Increasing concerns and regulations about data privacy necessitate the study of privacy-preserving, decentralized learning methods for natural language processing tasks. |
| Approach: | They propose a framework for evaluating federated learning methods on four different tasks . they propose federation between Transformer-based language models and FL methods . |
| Outcome: | The proposed framework compares FL methods on four different tasks under non-IID partitioning strategies. |
Creating a Lens of Chinese Culture: A Multimodal Dataset for Chinese Pun Rebus Art Understanding (2025.findings-acl)
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Tuo Zhang, Tiantian Feng, Yibin Ni, Mengqin Cao, Ruying Liu, Kiana Avestimehr, Katharine Butler, Yanjun Weng, Mi Zhang, Shrikanth Narayanan, Salman Avestimehr
| Challenge: | a new study examines the performance of large vision-language models in understanding art . the Pun Rebus Art Dataset is a multimodal dataset for art understanding rooted in traditional Chinese culture . |
| Approach: | They propose a multimodal dataset for art understanding deeply rooted in traditional Chinese culture . they aim to facilitate the development of VLMs that can better understand culturally specific content . |
| Outcome: | The proposed dataset shows that state-of-the-art VLMs struggle with these tasks . the data will facilitate the development of VLM models that can better understand culturally specific content . |
Reconsidering LLM Uncertainty Estimation Methods in the Wild (2025.acl-long)
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Yavuz Faruk Bakman, Duygu Nur Yaldiz, Sungmin Kang, Tuo Zhang, Baturalp Buyukates, Salman Avestimehr, Sai Praneeth Karimireddy
| Challenge: | Existing studies evaluate UE methods in short-form QA settings, but real-world deployment presents several challenges. |
| Approach: | They examine UE methods' sensitivity to decision threshold selection and their robustness to query transformations such as typos and adversarial prompts. |
| Outcome: | The proposed methods exhibit robustness against typos, adversarial prompts, and prior chat history, and are highly susceptible to adversarials. |
Revisiting OPRO: The Limitations of Small-Scale LLMs as Optimizers (2024.findings-acl)
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| Challenge: | Recent studies aim to enhance the efficacy of Large Language Models (LLMs) through strategic prompting. |
| Approach: | They propose to revisit the optimization by prompting approach for small-scale LLMs . they suggest future prompting engineering to consider both model capabilities and computational costs . |
| Outcome: | The proposed approach shows limited effectiveness in small-scale LLMs, with limited inference capabilities constraining optimization ability. |
MobiZO: Enabling Efficient LLM Fine-Tuning at the Edge via Inference Engines (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) are currently pre-trained and fine-tuned on large cloud servers . fine-timing on resource-constrained edge devices presents significant memory and computational demands . |
| Approach: | They propose a resource-efficient fine-tuning framework for LLMs specifically designed for edge devices. |
| Outcome: | Experiments show that MobiZO achieves substantial runtime speedups and memory savings while improving fine-tuning accuracy. |
Federated Learning with Noisy User Feedback (2022.naacl-main)
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Rahul Sharma, Anil Ramakrishna, Ansel MacLaughlin, Anna Rumshisky, Jimit Majmudar, Clement Chung, Salman Avestimehr, Rahul Gupta
| Challenge: | Artificial Intelligence (AI) and Machine Learning (ML) systems are becoming more popular and are causing concerns over user privacy. |
| Approach: | They propose a method for training ML models using positive and negative user feedback and a framework to extract labels on edge to make FL viable. |
| Outcome: | The proposed method improves significantly over a self-training baseline, achieving performance closer to models trained with full supervision. |
MARS: Meaning-Aware Response Scoring for Uncertainty Estimation in Generative LLMs (2024.acl-long)
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Yavuz Faruk Bakman, Duygu Nur Yaldiz, Baturalp Buyukates, Chenyang Tao, Dimitrios Dimitriadis, Salman Avestimehr
| Challenge: | Generative Large Language Models (LLMs) are widely utilized for their excellence in various tasks. however, their tendency to produce inaccurate or misleading outputs poses a potential risk. |
| Approach: | They propose a new scoring function that considers the semantic contribution of each token in the generated sequence in the context of the question. |
| Outcome: | The proposed scoring function improves UE performance on a medical QA dataset. |