OpenAI's chief executive Sam Altman will return to the company's board of directors after a probe into his brief sacking and subsequent rehiring. An investigation by law firm WilmerHale found that Altman's conduct "did not mandate his removal" last year, OpenAI said in a blog post on Friday. Altman's firing was instead due to a "breakdown in the relationship and loss of trust" between the 38-year-old entrepreneur and the previous board, the company said. OpenAI said it had "full confidence" in Altman's ongoing leadership at the artificial intelligence startup after reviewing the law firm's findings. "WilmerHale found that the prior Board acted within its broad discretion to terminate Mr. Altman, but also found that his conduct did not mandate removal," the company said in a summary of the report.

OpenAI Chief Executive Officer Sam Altman was cleared of any wrongdoing that would have mandated being fired from the company late last year, according to a report based on a monthslong investigation into the events leading up to his brief ouster. WilmerHale, the law firm that conducted the inquiry, found that the board's decision to fire Altman was a "consequence of a breakdown in the relationship and loss of trust between the prior Board and Mr. Altman," the artificial intelligence startup said Friday. It did not arise from concerns about product safety, the pace of development, or OpenAI's finances, it said. Altman is rejoining the company's board following the findings, the artificial intelligence startup said. OpenAI's board is also adding Sue Desmond-Hellmann, who previously was head of the Bill and Melinda Gates Foundation; Nicole Seligman, an ex-Sony Entertainment executive; and Instacart CEO Fidji Simo.

Sam Altman is back on the board of OpenAI, nearly four months after the CEO was ousted, and quickly reinstated, from the company he founded. Although Altman had returned as the AI company's top executive in November, a temporary board oversaw his return and the subsequent investigation into his conduct. That investigation is now complete, according to the company, which added three new members to its board of directors. The additions include: Instacart CEO and former Meta executive Fidji Simo, former Sony executive Nicole Seligman and Dr. Sue Desmond-Hellmann, former CEO of the Bill and Melinda Gates Foundation. Salesforce co-CEO Bret Taylor, economist Larry Summers and OpenAI co-founder Greg Brockman, who served on the temporary three-seat board, will remain in their positions with Taylor continuing as chair.

OpenAI is reinstating CEO Sam Altman to its board of directors and said it has "full confidence" in his leadership after an outside investigation into the turmoil that led the company to abruptly fire and rehire him in November. OpenAI said the investigation by the law firm WilmerHale concluded that Altman's ouster had been a "consequence of a breakdown in the relationship and loss of trust" between Altman and the prior board. The ChatGPT maker also said it has added three women to its board of directors: Sue Desmond-Hellman, a former CEO of the Bill & Melinda Gates Foundation; Nicole Seligman, a former Sony general counsel; and Instacart CEO Fidji Simo. The actions are a way for the San Francisco-based artificial intelligence company to show investors and customers that it is trying to move past the internal conflicts that nearly destroyed it last year and made global headlines. "I'm pleased this whole thing is over," Altman told reporters Friday, adding that he's been disheartened to see people leaking information to try to "pit us against each other" and demoralize the team. At the same time, he said he's learned from the experience and apologized for a dispute with a former board member he could have handled "with more grace and care".

Conversational systems are crucial for human-computer interaction, managing complex dialogues by identifying threads and prioritising responses. This is especially vital in multi-party conversations, where precise identification of threads and strategic response prioritisation ensure efficient dialogue management. To address these challenges an end-to-end model that identifies threads and prioritises their response generation based on the importance was developed, involving a systematic decomposition of the problem into discrete components - thread detection, prioritisation, and performance optimisation which was meticulously analysed and optimised. These refined components seamlessly integrate into a unified framework, in conversational systems. Llama2 7b is used due to its high level of generalisation but the system can be updated with any open source Large Language Model(LLM). The computational capabilities of the Llama2 model was augmented by using fine tuning methods and strategic prompting techniques to optimise the model's performance, reducing computational time and increasing the accuracy of the model. The model achieves up to 10x speed improvement, while generating more coherent results compared to existing models.

Transformers are the backbone of powerful foundation models for many Vision and Natural Language Processing tasks. But their compute and memory/storage footprint is large, and so, serving such models is expensive often requiring high-end hardware. To mitigate this difficulty, Post-Training Quantization seeks to modify a pre-trained model and quantize it to eight bits or lower, significantly boosting compute/memory/latency efficiency. Such models have been successfully quantized to four bits with some performance loss. In this work, we outline a simple scheme to quantize Transformer-based models to just two bits (plus some overhead) with only a small drop in accuracy. Key to our formulation is a concept borrowed from Harmonic analysis called Fusion Frames. Our main finding is that the quantization must take place not in the original weight space, but instead in the Fusion Frame representations. If quantization is interpreted as the addition of noise, our casting of the problem allows invoking an extensive body of known consistent recovery and noise robustness guarantees. Further, if desired, de-noising filters are known in closed form. We show empirically, via a variety of experiments, that (almost) two-bit quantization for Transformer models promises sizable efficiency gains.

As large language models (LLMs) are increasingly deployed in user-facing applications, building trust and maintaining safety by accurately quantifying a model's confidence in its prediction becomes even more important. However, finding effective ways to calibrate LLMs - especially when the only interface to the models is their generated text - remains a challenge. We propose APRICOT (auxiliary prediction of confidence targets): A method to set confidence targets and train an additional model that predicts an LLM's confidence based on its textual input and output alone. This approach has several advantages: It is conceptually simple, does not require access to the target model beyond its output, does not interfere with the language generation, and has a multitude of potential usages, for instance by verbalizing the predicted confidence or adjusting the given answer based on the confidence. We show how our approach performs competitively in terms of calibration error for white-box and black-box LLMs on closed-book question-answering to detect incorrect LLM answers.

Deep phenotyping is the detailed description of patient signs and symptoms using concepts from an ontology. The deep phenotyping of the numerous physician notes in electronic health records requires high throughput methods. Over the past thirty years, progress toward making high throughput phenotyping feasible. In this study, we demonstrate that a large language model and a hybrid NLP model (combining word vectors with a machine learning classifier) can perform high throughput phenotyping on physician notes with high accuracy. Large language models will likely emerge as the preferred method for high throughput deep phenotyping of physician notes.

Despite advancements in on-topic dialogue systems, effectively managing topic shifts within dialogues remains a persistent challenge, largely attributed to the limited availability of training datasets. To address this issue, we propose Multi-Passage to Dialogue (MP2D), a data generation framework that automatically creates conversational question-answering datasets with natural topic transitions. By leveraging the relationships between entities in a knowledge graph, MP2D maps the flow of topics within a dialogue, effectively mirroring the dynamics of human conversation. It retrieves relevant passages corresponding to the topics and transforms them into dialogues through the passage-to-dialogue method. Through quantitative and qualitative experiments, we demonstrate MP2D's efficacy in generating dialogue with natural topic shifts. Furthermore, this study introduces a novel benchmark for topic shift dialogues, TS-WikiDialog. Utilizing the dataset, we demonstrate that even Large Language Models (LLMs) struggle to handle topic shifts in dialogue effectively, and we showcase the performance improvements of models trained on datasets generated by MP2D across diverse topic shift dialogue tasks.

Recent advancements in pretraining have demonstrated that modern Large Language Models (LLMs) possess the capability to effectively learn arithmetic operations. However, despite acknowledging the significance of digit order in arithmetic computation, current methodologies predominantly rely on sequential, step-by-step approaches for teaching LLMs arithmetic, resulting in a conclusion where obtaining better performance involves fine-grained step-by-step. Diverging from this conventional path, our work introduces a novel strategy that not only reevaluates the digit order by prioritizing output from the least significant digit but also incorporates a step-by-step methodology to substantially reduce complexity. We have developed and applied this method in a comprehensive set of experiments. Compared to the previous state-of-the-art (SOTA) method, our findings reveal an overall improvement of in accuracy while requiring only a third of the tokens typically used during training. For the purpose of facilitating replication and further research, we have made our code and dataset publicly available at \url{https://anonymous.4open.science/r/RAIT-9FB7/}.