Papers by Richard Zemel
Training-free Deep Concept Injection Enables Language Models for Video Question Answering (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to train pretrained language models for zero-shot crossmodal tasks require crossmodal pretraining. |
| Approach: | They propose to inject visual concepts into the input text embedding space of a pretrained language model and build adaptation layers based on the intermediate representation of concepts. |
| Outcome: | The proposed model performs zero-shot crossmodal tasks without crossmodal pretraining . it is based on the injection of visual concepts as input tokens and augmentation in intermediate features . the proposed model achieves competitive or even better results in zero- shot and fine-tuning settings . |
Whiteboard-of-Thought: Thinking Step-by-Step Across Modalities (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large language models have shown promising results in arithmetic and symbolic reasoning by expressing intermediate reasoning in text as a chain of thought, yet struggle to extend this capability to answer text queries that are easily solved by visual reasoning. |
| Approach: | They propose a method to unlock the visual reasoning capabilities of multimodal large language models by using a metaphorical ‘whiteboard’ to draw out reasoning steps as images and return these images back to the model for further processing. |
| Outcome: | The proposed method shows that it can be used on four difficult tasks that involve visual and spatial reasoning with no demonstrations or specialized modules. |
Coordinated Replay Sample Selection for Continual Federated Learning (2023.emnlp-industry)
Copied to clipboard
Jack Good, Jimit Majmudar, Christophe Dupuy, Jixuan Wang, Charith Peris, Clement Chung, Richard Zemel, Rahul Gupta
| Challenge: | Continual Federated Learning (CFL) combines decentralized learning with continuous learning . ubiquity of personal devices with a network connection offers rich source of data for learning problems . |
| Approach: | They propose to combine decentralized learning with a continuous learning approach . they propose to coordinate gradient-based replay sample selection across clients . |
| Outcome: | The proposed method shows gains early in the low replay size regime, when the budget for storing past data is small. |
A Computational Framework for Slang Generation (2021.tacl-1)
Copied to clipboard
| Challenge: | Existing language models trained on large text corpora are biased toward formal language and under-represent slang. |
| Approach: | They propose a framework that models the speaker’s word choice in slang context by relating the conventional and sexist senses of a word while incorporating syntactic and contextual knowledge. |
| Outcome: | The proposed framework outperforms state-of-the-art language models and better predicts the historical emergence of slang word usages from 1960s to 2000s. |
The steerability of large language models toward data-driven personas (2024.naacl-long)
Copied to clipboard
Junyi Li, Charith Peris, Ninareh Mehrabi, Palash Goyal, Kai-Wei Chang, Aram Galstyan, Richard Zemel, Rahul Gupta
| Challenge: | Large language models generate biased responses where opinions of certain groups and populations are underrepresented. |
| Approach: | They propose a data-driven notion of persona that allows for a more nuanced understanding of different (latent) social groups present in the population. |
| Outcome: | The proposed method improves model steerability by 57% over baselines. |
Attribute Controlled Fine-tuning for Large Language Models: A Case Study on Detoxification (2024.findings-emnlp)
Copied to clipboard
Tao Meng, Ninareh Mehrabi, Palash Goyal, Anil Ramakrishna, Aram Galstyan, Richard Zemel, Kai-Wei Chang, Rahul Gupta, Charith Peris
| Challenge: | Using a sequence-level constraint, we regularize the LLMtraining by penalizing the KL divergence between the desired output distribution and the LRM’s posterior. |
| Approach: | They propose a constraint learning schema forfine-tuning Large Language Models with attribute control by penalizing the KL divergence be-tween the desired output distribution and the LLM's posterior. |
| Outcome: | The proposed approach improves the performance of large language models while enhancing their utility and generation quality. |
FLIRT: Feedback Loop In-context Red Teaming (2024.emnlp-main)
Copied to clipboard
Ninareh Mehrabi, Palash Goyal, Christophe Dupuy, Qian Hu, Shalini Ghosh, Richard Zemel, Kai-Wei Chang, Aram Galstyan, Rahul Gupta
| Challenge: | Recent work has evaluated the vulnerabilities of large generative models, such as DALL-E, ChatGPT, and GPT-4. |
| Approach: | They propose an automatic red teaming framework that evaluates a given black-box model and exposes its vulnerabilities against unsafe and inappropriate content generation. |
| Outcome: | The proposed framework evaluates a given black-box model and exposes its vulnerabilities against unsafe and inappropriate content generation. |
Tokenization Matters: Navigating Data-Scarce Tokenization for Gender Inclusive Language Technologies (2024.findings-naacl)
Copied to clipboard
Anaelia Ovalle, Ninareh Mehrabi, Palash Goyal, Jwala Dhamala, Kai-Wei Chang, Richard Zemel, Aram Galstyan, Yuval Pinter, Rahul Gupta
| Challenge: | a recent study documented the harmful limitations of gender binary-centric large language models . data scarcity is a known culprit, but the precise mechanisms through which scarcity affects this behavior remain underexplored. |
| Approach: | They propose to use BPE tokenization to enforce consistent tokenization across gendered pronouns to improve neopronoun proficiency. |
| Outcome: | The proposed methods outperform finetuning with standard BPE, and improve neopronoun proficiency. |
Toward Informal Language Processing: Knowledge of Slang in Large Language Models (2024.naacl-long)
Copied to clipboard
| Challenge: | Recent advances in large language models (LLMs) have offered a strong potential for natural language systems to process informal language. |
| Approach: | They propose to use movie subtitles to evaluate slang in large language models . they find that smaller LLMs finetuned on the dataset achieve comparable performance . |
| Outcome: | The proposed dataset can be used to evaluate LLMs on slang detection and identification of regional and historical sources for interpretive insights. |
Semantically Informed Slang Interpretation (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing approaches to slang interpretation rely on context but ignore semantic extensions common in slings . a semantically informed slapping framework can be applied to enhancing machine translation of informal language . |
| Approach: | They propose a semantically informed slang interpretation framework that considers contextual and semantic appropriateness of a candidate interpretation for a query s. |
| Outcome: | The proposed framework achieves state-of-the-art accuracy in slang interpretation in English and in other languages. |
Resolving Ambiguities in Text-to-Image Generative Models (2023.acl-long)
Copied to clipboard
Ninareh Mehrabi, Palash Goyal, Apurv Verma, Jwala Dhamala, Varun Kumar, Qian Hu, Kai-Wei Chang, Richard Zemel, Aram Galstyan, Rahul Gupta
| Challenge: | ambiguities can lead to misinterpretation and miscommunication in natural language . resolving ambiguity is notoriously hard for machines . |
| Approach: | They propose a framework to disambiguate prompts given to generative models by soliciting clarifications from the end user. |
| Outcome: | The proposed framework generates more faithful images better aligned with user intention in the presence of ambiguities. |
Towards Safety Reasoning in LLMs: AI-agentic Deliberation for Policy-embedded CoT Data Creation (2025.findings-acl)
Copied to clipboard
Tharindu Kumarage, Ninareh Mehrabi, Anil Ramakrishna, Xinyan Zhao, Richard Zemel, Kai-Wei Chang, Aram Galstyan, Rahul Gupta, Charith Peris
| Challenge: | Safety reasoning paradigms require high-quality policy-embedded chain-of-thought datasets . generating such data through human annotations is prohibitively expensive . |
| Approach: | They propose AIDSAFE: Agentic Iterative Deliberation for Safety Reasoning . AIDS AFE leverages multi-agent deliberation to iteratively expand reasoning on safety policies . |
| Outcome: | The proposed model improves policy adherence and reasoning quality while maintaining acceptable utility and over-refusal accuracy. |