Papers by Morteza Ziyadi
Reasoning Like Program Executors (2022.emnlp-main)
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Xinyu Pi, Qian Liu, Bei Chen, Morteza Ziyadi, Zeqi Lin, Qiang Fu, Yan Gao, Jian-Guang Lou, Weizhu Chen
| Challenge: | Existing language models are inadequate in reasoning, according to studies . a new reasoning pre-training paradigm is based on pretraining language models with programs . |
| Approach: | They propose a reasoning pre-training paradigm that empowers language models to harvest reasoning knowledge possessed by program executors. |
| Outcome: | The proposed reasoning pre-training paradigm can boost models' reasoning skills . it can be instantiated by different kinds of program executors and run on a single database . |
On Localizing and Deleting Toxic Memories in Large Language Models (2025.findings-naacl)
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Anubrata Das, Manoj Kumar, Ninareh Mehrabi, Anil Ramakrishna, Anna Rumshisky, Kai-Wei Chang, Aram Galstyan, Morteza Ziyadi, Rahul Gupta
| Challenge: | Existing methods to reduce toxic generation in large language models are not fully understood. |
| Approach: | They propose to understand the mechanisms that drive toxic generation in large language models by using memory localization to reduce toxic generation. |
| Outcome: | The proposed method reduces toxic generation from 62.86% to 28.61%, but it also improves generation quality. |
INVITE: a Testbed of Automatically Generated Invalid Questions to Evaluate Large Language Models for Hallucinations (2023.findings-emnlp)
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| Challenge: | Recent advances in Large language models have enabled them to hold free form conversations over multiple turns, but they exhibit a tendency to make unfounded and incorrect statements, commonly labeled as hallucinations. |
| Approach: | They propose a framework to test large language models for hallucinations using automatically generated INValId questions. |
| Outcome: | The proposed framework is based on a testbed of automatically generated INValId questions to evaluate large language models for hallucinations. |
Entity Contrastive Learning in a Large-Scale Virtual Assistant System (2023.acl-industry)
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| Challenge: | Named Entity Recognition (NER) tasks are a well-studied and fundamental task within Natural Language Understanding (NLU). |
| Approach: | They propose to incorporate entity contrastive learning into a virtual assistant system to improve NER models by clustering similar inputs closer together in a learned representation space. |
| Outcome: | The proposed model improves against a production baseline system that does not use contrastive learning. |
Improving Large-Scale Conversational Assistants using Model Interpretation based Training Sample Selection (2022.emnlp-industry)
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Stefan Schroedl, Manoj Kumar, Kiana Hajebi, Morteza Ziyadi, Sriram Venkatapathy, Anil Ramakrishna, Rahul Gupta, Pradeep Natarajan
| Challenge: | Large-scale, voice-based conversational assistants process each utterance through a multi-stage pipeline that includes wakeword detection, automatic speech recognition (ASR), natural language understanding (NLU), entity resolution, and textto-speech. |
| Approach: | They propose a method to identify customer implicitly satisfied with Alexa's responses by leveraging interpretations of model behavior. |
| Outcome: | The proposed approach produces statistically significant improvements in both offline and online tests. |
RAGPPI: Retrieval-Augmented Generation Benchmark for Protein–Protein Interactions in Drug Discovery (2026.eacl-long)
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| Challenge: | Large Language Models and Retrieval-Augmented Generation (RAG) frameworks have supported Target ID, but no benchmark exists for identifying biological impacts of PPIs. |
| Approach: | They propose to build a factual question-answer benchmark of 4,420 question-announced pairs that focus on the potential biological impacts of PPIs. |
| Outcome: | The proposed benchmark is based on 4,420 question-answer pairs with expert-driven data annotation. |