Papers by Seyed Ali Bahrainian
Are LLMs effective psychological assessors? Leveraging adaptive RAG for interpretable mental health screening through psychometric practice (2025.acl-long)
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| Challenge: | standardized questionnaires are essential tools for mental health screening, but computational approaches bypass these tools in favor of black-box classification. |
| Approach: | They propose a questionnaire-guided screening framework that bridges psychological practice and computational methods through adaptive Retrieval-Augmented Generation. |
| Outcome: | The proposed framework matches or outperforms state-of-the-art performance on Reddit-based benchmarks and extends to self-harm screening. |
PersonalityDBench: A Dataset for Personality Disorders - from Modeling to Controlled Generation (2026.acl-long)
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| Challenge: | Personality disorders are chronic, rigid patterns of thinking, behavior, and emotions that deviate from cultural norms and persist in social settings. |
| Approach: | They propose a large-scale, clinically grounded dataset that supports multidimensional study of personality pathology and standardized evaluation of LLM steering toward clinically ground behavioral targets. |
| Outcome: | The PersonalityDBench dataset supports multidimensional study of personality pathology and evaluation of LLM steering toward clinically grounded behavioral targets. |
Enhancing Retrieval-Augmented Generation: A Study of Best Practices (2025.coling-main)
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| Challenge: | Retrieval-augmented generation systems have shown remarkable advancements by integrating retrieval mechanisms into language models, enhancing their ability to produce more accurate and contextually relevant responses. |
| Approach: | They propose to integrate query expansion, various novel retrieval strategies, and a Contrastive In-Context Learning RAG to improve response quality. |
| Outcome: | The proposed RAGs incorporate query expansion, various novel retrieval strategies, and a novel Contrastive In-Context Learning RAG. |
Interpretability Analysis of Arithmetic In-Context Learning in Large Language Models (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) solve arithmetic with only a few in-context examples, yet the computations that connect those examples to the answer remain opaque. |
| Approach: | They propose to use in-context examples to illustrate how large language models process ICEs to isolate partial-sum representations in three-operand tasks and investigate their influence on final logits. |
| Outcome: | The proposed model performs better than previous models on three-operand tasks. |
Self-Supervised Neural Topic Modeling (2021.findings-emnlp)
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| Challenge: | Topic models are useful tools for analyzing and interpreting the main underlying themes of large corpora of text. |
| Approach: | They propose a self-supervised neural topic model that learns a topic representation jointly from three co-occurring words and a document that the triple originates from. |
| Outcome: | The proposed model outperforms existing topic models in coherence metrics and document clustering accuracy. |
NEWTS: A Corpus for News Topic-Focused Summarization (2022.findings-acl)
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| Challenge: | Existing benchmarking corpora provide concordant pairs of full and abridged versions of Web, news or professional content. |
| Approach: | They propose a topical summarization corpus called NEWTS that is annotated via crowd-sourcing. |
| Outcome: | The proposed model can condition summaries on a desired range of themes . the proposed model outperforms Lead-3 baselines on most benchmark datasets . |
MATCHA: Matching Text via Contrastive Semantic Alignment (2026.findings-acl)
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| Challenge: | MATCHA is an automatic metric that rewards semantic agreement with a reference and penalizes contradictions. |
| Approach: | They introduce a metric that jointly rewards semantic agreement with a reference and penalizes contradictions. |
| Outcome: | The proposed metric outperforms popular metrics on eight public benchmarks compared with human annotations on question-answering, image caption generation, natural language inference, summarization, and semantic textual similarity tasks. |
SIMSUM: Document-level Text Simplification via Simultaneous Summarization (2023.acl-long)
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| Challenge: | Document-level text simplification is a specific type of simplification which involves simplifying documents consisting of several sentences by rewriting them into fewer or more sentences. |
| Approach: | They propose a new two-stage framework SIMSUM for automated document-level text simplification which uses explicit summarization and simplification models and guides the generation using the main keywords of a source text. |
| Outcome: | The proposed model outperforms baseline models on two document-level simplification datasets, namely D-Wikipedia and Wiki-Doc. |
Text Simplification via Adaptive Teaching (2024.findings-acl)
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| Challenge: | Text simplification is the process of rewriting a text using simpler vocabulary and grammatical structure in order to make it more accessible and understandable for a larger audience. |
| Approach: | They propose a model for text simplification based on adaptive teaching using a teacher network and a text generation network. |
| Outcome: | The proposed model outperforms the current state-of-the-art model on the Wiki-Doc and D-Wikipedia datasets and performs well on human evaluations in terms of text simplicity, correctness, and fluency. |
Beyond Multiple Choice: Evaluating Steering Vectors for Summarization (2026.findings-eacl)
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| Challenge: | Recent methods for controlling language models can often be classified into three main strategies: prompt engineering, trainable decoding mechanisms, fine-tuning according to specific objectives. |
| Approach: | They evaluate steering vectors for controlling topical focus, sentiment, toxicity, and readability in abstractive summaries across the SAMSum, NEWTS, and arXiv datasets. |
| Outcome: | The proposed method is effective in free-form generation, but high steering strengths induce degenerate repetition and factual hallucinations. |