Papers by Elman Mansimov
Pre-training Intent-Aware Encoders for Zero- and Few-Shot Intent Classification (2023.emnlp-main)
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Mujeen Sung, James Gung, Elman Mansimov, Nikolaos Pappas, Raphael Shu, Salvatore Romeo, Yi Zhang, Vittorio Castelli
| Challenge: | Existing methods for IC training do not provide sufficient examples for each intent . a novel pre-training method is proposed to provide a better understanding of intents . |
| Approach: | They propose a method that uses contrastive learning with intent psuedo-labels to produce embeddings that are well-suited for IC tasks. |
| Outcome: | The proposed method achieves 5.4% and 4.0% higher accuracy than the current state-of-the-art method on four IC datasets. |
Bootstrapping LLM-based Task-Oriented Dialogue Agents via Self-Talk (2024.findings-acl)
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| Challenge: | Large language models (LLMs) are powerful dialogue agents, but specializing them towards fulfilling a specific function can be prohibitive in terms of feasibility, time, and resources. |
| Approach: | They propose a method for training large language models by enabling "self-talk" they propose supervised fine-tuning of LLMs to improve quality of dialogues . |
| Outcome: | The proposed method generates training data via "self-talk" of LLMs that can be refined and utilized for supervised fine-tuning. |
Backward Compatibility During Data Updates by Weight Interpolation (2024.eacl-long)
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| Challenge: | Retraining a model with a larger amount of training data introduces negative flips . retraining the model with the updated data introduce negative flipping . |
| Approach: | They propose a backward compatible weight interpolation method to improve model predictions without regression bugs. |
| Outcome: | The proposed method reduces negative flips without sacrificing accuracy . it is straight forward to implement and does not increase inference cost. |
Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System (2022.acl-long)
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| Challenge: | Existing pre-trained language models often form a cascaded generation problem . this can lead to error accumulation across different sub-tasks and greater data annotation overhead. |
| Approach: | They propose a plug-and-play model for task-oriented dialogue that learns primary TOD task completion skills from heterogeneous dialog corpora. |
| Outcome: | The proposed model learns primary TOD task completion skills from heterogeneous dialog corpora. |
Improving Prediction Backward-Compatiblility in NLP Model Upgrade with Gated Fusion (2023.findings-eacl)
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| Challenge: | a regression error during model upgrade often outweighs the benefits of accuracy gain . a novel method that promotes backward compatibility during model upgrades is proposed . |
| Approach: | They propose a method that promotes backward compatibility via learning to mix predictions between old and new models. |
| Outcome: | The proposed method outperforms existing methods and achieves negative flip rate reductions by 73.2% on two model upgrade scenarios. |
Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement (D18-1)
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| Challenge: | Despite its success, neural autoregressive modeling has its weakness in decoding, i.e., finding the most likely sequence. |
| Approach: | They propose a conditional non-autoregressive neural sequence model based on iterative refinement based upon latent variable models and conditional denoising autoencoders. |
| Outcome: | The proposed model significantly speeds up decoding while maintaining the generation quality comparable to the autoregressive counterpart. |
DREAM: Deep Research Evaluation with Agentic Metrics (2026.acl-long)
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Elad Ben Avraham, ChangHao Li, Ron Dorfman, Roy Ganz, Oren Nuriel, Amir Dudai, Aviad Aberdam, Noah Flynn, Elman Mansimov, Aditya Kalyanpur, Ron Litman
| Challenge: | Recent benchmarks propose distinct methodologies, yet they suffer from the Mirage of Synthesis . static evaluators lack the tool-use capabilities required to assess temporal validity and factual correctness . |
| Approach: | They propose a framework that instantiates the principle of capability parity by making evaluation agentic. |
| Outcome: | The proposed framework is more sensitive to factual decay than existing benchmarks . large language models increasingly support autonomous, tool-using agents . |
Label Semantic Aware Pre-training for Few-shot Text Classification (2022.acl-long)
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| Challenge: | Existing models for text classification use label semantics but few studies have attempted to give models access to informative representations of labels. |
| Approach: | They propose to use label semantics to train generative models by performing secondary pre-training on labeled sentences from a variety of domains. |
| Outcome: | The proposed approach improves generalization and data efficiency of text classification systems while maintaining comparable performance to state-of-the-art models. |