Large Language Models (LLMs) are prone to hallucinations, producing fluent but factually incorrect statements. Recent multi-agent debate methods improve hallucination detection by jointly improving reasoning and decision-making. However, existing approaches either collaborate which amplifies shared overconfidence, or adopt adversarial preset stances, that can inject incorrect information complicating decision making. To address this, we propose Contradictory Statement Multi-Agent Debate (CSMAD), a multi-agent framework that creates structured disagreement by generating a contradictory claim for each input claim. CSMAD asks independent agents to evaluate the claim and the contradictory claim, which encourages different lines of reasoning without assigning preset stances. When the outcome is non-discriminative; both the contradictory statements are either accepted or rejected; the agents exchange rationales and update their judgments after considering opposing evidence. A final judge then decides the truth of the original claim, using both arguments as context. To make contradictory statement generation reliable, we add a Natural Language Inference (NLI) based verifier that checks whether the generated statement actually contradicts the original claim; if it does not, the system falls back to an explicit negation-based contradiction. Across public benchmarks for question answering and scientific claim verification, as well as a proprietary e-commerce claims dataset, we show that CSMAD consistently outperforms the strongest baseline for both large (Claude-3.5 Sonnet) and medium-sized (Qwen3-8B) language models, improving F1 by +2.3 and +4.1 points, respectively, while reducing LLM token cost by 28%.

Product recommendations incentivize customers to make multi-unit purchases by surfacing relevant products, leading to lower cost per unit for e-commerce stores and lower prices for their customers. However, the humongous scale of products, implicit co-purchase asymmetry and variation in co-purchase behavior across different categories, are orthogonal problems to solve. To address these problems, we propose MERLIN (Multimodal & Multilingual Embedding for Recommendations at Large-scale via Item associations), a Graph Neural Network that generates product recommendations from a heterogeneous and directed product graph. We mine category associations to remove noisy product co-purchase associations, leading to higher quality recommendations. Leveraging product co-view relationships, we finetune SentenceBERT model for textual representation, and train a self-supervised knowledge distillation model to learn visual representation, which allows us to learn product representations which are multi-lingual and multi-modal in nature. We selectively align node embeddings leveraging co-viewed products. MERLIN model can handle node asymmetry by learning dual embeddings for each product, and can generate recommendations for cold-start products by employing catalog metadata such as title, category and image. Extensive offline experiments on internal and external datasets show that MERLIN model outperforms state-of-the-art baselines for node recommendation and link prediction task. We conduct ablations to quantify the impact of our model components and choices. Further, MERLIN model delivers significant improvement in sales measured through an A/B experiment.

Swapnil Gupta, Sreyash Kenkre, Partha Talukdar. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

In this paper, we present a novel scheme for text-independent online writer identification. As a first contribution, we propose histogram based features, inspired from the area of object detection, to describe the structural primitives of handwriting. Secondly, we have used sparse coding techniques to learn prototypes, that describe the general writing characteristics of the authors. To the best of our knowledge, the present proposal is the first of its kind that exploits the sparse learning framework for online writer identification. In addition, we consider the inclusion of ideas from information retrieval into our sparse representation to formulate a novel descriptor for each document. The efficacy of our proposal is tested on the handwritten paragraphs and text lines of the IAM On-Line Handwriting Database. We also provide a quantitative comparison of performance of our histogram based features with Fourier and Wavelet descriptors. The results are promising.