Faithful generation in large language models (LLMs) is challenged by knowledge conflicts between parametric memory and external context.Existing contrastive decoding methods tuned specifically to handle conflict often lack adaptability and can degrade performance in low conflict settings.We introduce COCOA (Confidence-and Context-Aware Adaptive Decoding), a novel token-level algorithm for principled conflict resolution and enhanced faithfulness.COCOA resolves conflict by utilizing confidence-aware measures (entropy gap and contextual peakedness) and the generalized divergence between the parametric and contextual distributions.Crucially, COCOA maintains strong performance even in low conflict settings.Extensive experiments across multiple LLMs on diverse Question Answering (QA), Summarization, and Long-Form Question Answering (LFQA) benchmarks demonstrate CO-COA's state-of-the-art performance over strong baselines like ADACAD.It yields significant gains in QA accuracy, up to 9.2 points on average compared to the strong baseline ADACAD, and improves factuality in summarization and LFQA by up to 2.5 points on average across key benchmarks.Additionally, it demonstrates superior sensitivity to conflict variations.CO-COA enables more informed, context-aware, and ultimately more faithful token generation.High Conflict Context Tom Hanks played multiple roles in Polar Express, including Conductor, Hero Boy... Dylan Cash as Boy on Train Dante Pastula played the Li�le Boy. QuestionWho played the little boy in Polar Express? Parametric Knowledge (LM)The LM believes Daryl Sabara played the Little Boy in Polar Express.

In recent years, the fields of natural language processing (NLP) and information retrieval (IR) have made tremendous progress thanks to deep learning models like Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTMs) networks, and Transformer [ 121 ] based models like Bidirectional Encoder Representations from Transformers (BERT) [ 24 ], Generative Pre-training Transformer (GPT-2) [ 95 ], Multi-task Deep Neural Network (MT-DNN) [ 74 ], Extra-Long Network (XLNet) [ 135 ], Text-to-text transfer transformer (T5) [ 96 ], T-NLG [ 99 ], and GShard [ 64 ]. But these models are humongous in size. On the other hand, real-world applications demand small model size, low response times, and low computational power wattage. In this survey, we discuss six different types of methods (Pruning, Quantization, Knowledge Distillation (KD), Parameter Sharing, Tensor Decomposition, and Sub-quadratic Transformer-based methods) for compression of such models to enable their deployment in real industry NLP projects. Given the critical need of building applications with efficient and small models, and the large amount of recently published work in this area, we believe that this survey organizes the plethora of work done by the “deep learning for NLP” community in the past few years and presents it as a coherent story.

Lack of comprehensive information on frequently asked questions (FAQ) web pages forces users to pose their questions on community question answering forums or contact businesses over slow media like emails or phone calls. This in turn often results into sub-optimal user experience and opportunity loss for businesses. While previous work focuses on FAQ mining and answering queries from FAQ pages, there is no work on verifying completeness or augmenting FAQ pages. We present a system, called FAQAugmenter, which given an FAQ web page, (1) harnesses signals from query logs and the web corpus to identify missing topics, and (2) suggests ranked list of questions for FAQ web page augmentation. Our experiments with FAQ pages from five enterprises each across three categories (banks, hospitals and airports) show that FAQAugmenter suggests high quality relevant questions. FAQAugmenter will contribute significantly not just in improving quality of FAQ web pages but also in turn improving quality of downstream applications like Microsoft QnA Maker.

In this work, we highlight some interesting challenges faced when trying to build a large-scale commercial IR-based chatbot, Ruuh, for an emerging market like India which has unique characteristics such as high linguistic and cultural diversity, large section of young population and the second largest mobile market in the world. We set out to build a "human-like" AI agent which aspires to become the trusted friend of every Indian youth. To meet this objective, we realised that we need to think beyond the utilitarian notion of merely generating "relevant" responses and enable the agent to comprehend and meet a wider range of user social needs, like expressing happiness when user's favourite team wins, sharing a cute comment on showing the pictures of the user's pet and so on. The agent should also be well-versed with the informal language of the urban Indian youth which often includes slang and code-mixing across two or more languages (English and their native language). Finally, in order to be their trusted friend, the agent has to communicate with respect without offending their sentiments and emotions. Some of the above objectives pose significant research challenges in the areas of NLP, IR and AI. We take the audience through our journey of how we tackled some of the above challenges while building a large-scale commercial IR-based conversational agent. Our attempts to solve some of the above challenges have also resulted in some interesting research contributions in the form of publications and patents in the above areas. Our chat-bot currently has more than 1M users who have engaged in more than 70M conversations.