Large Language Models (LLMs) have shown remarkable capabilities across tasks, yet they often require additional prompting techniques when facing complex problems. While approaches like self-correction and response selection have emerged as popular solutions, recent studies have shown these methods perform poorly when relying on the LLM itself to provide feedback or selection criteria. We argue this limitation stems from the fact that common LLM post-training procedures lack explicit supervision for discriminative judgment tasks. In this paper, we propose Generative Self-Aggregation (GSA), a novel prompting method that improves answer quality without requiring the model's discriminative capabilities. GSA first samples multiple diverse responses from the LLM, then aggregates them to obtain an improved solution. Unlike previous approaches, our method does not require the LLM to correct errors or compare response quality; instead, it leverages the model's generative abilities to synthesize a new response based on the context of multiple samples. While GSA shares similarities with the self-consistency (SC) approach for response aggregation, SC requires specific verifiable tokens to enable majority voting. In contrast, our approach is more general and can be applied to open-ended tasks. Empirical evaluation demonstrates that GSA effectively improves response quality across various tasks, including mathematical reasoning, knowledge-based problems, and open-ended generation tasks such as code synthesis and conversational responses.

Social media is a great source of data for users reporting information and regarding their health and how various things have had an effect on them. This paper presents various approaches using Transformers and Large Language Models and their ensembles, their performance along with advantages and drawbacks for various tasks of SMM4H'24 - Classifying texts on impact of nature and outdoor spaces on the author's mental health (Task 3), Binary classification of tweets reporting their children's health disorders like Asthma, Autism, ADHD and Speech disorder (task 5), Binary classification of users self-reporting their age (task 6).

Large language models (LLMs) still grapple with complex tasks like mathematical reasoning. Despite significant efforts invested in improving prefix prompts or reasoning process, the crucial role of problem context might have been neglected. Accurate recognition of inputs is fundamental for solving mathematical tasks, as ill-formed problems could potentially mislead LLM's reasoning. In this study, we propose a new approach named Problem Elaboration Prompting (PEP) to enhance the mathematical capacities of LLMs. Specifically, PEP decomposes and elucidates the problem context before reasoning, therefore enhancing the context modeling and parsing efficiency. Experiments across datasets and models demonstrate promising performances: (1) PEP demonstrates an overall enhancement in various mathematical tasks. For instance, with the GPT-3.5 model, PEP exhibits improvements of 9.93% and 8.80% on GSM8k through greedy decoding and self-consistency, respectively. (2) PEP can be easily implemented and integrated with other prompting methods. (3) PEP shows particular strength in handling distraction problems.

The Personal Computer and the Internet have, without a doubt, revolutionized business in the last 25 years. For most of us, it's now impossible to think of the business world without all of the technology and the services built on top of these two innovations. Yet, what if these innovations are just teaser of what is to come in the next couple of decades? While it's certainly true that innovation isn't always a purely positive force, technological innovation has still been the biggest factor in driving long-term economic growth and improving the quality of life since the dawn of mankind. The agricultural and industrial revolutions, as well as the adoption of electricity have clearly been huge drivers of productivity for our society.

Summary: Just where are we in the Age of AI, where are we going, and what happens when we get there? When things are changing fast, sometimes it's necessary to take a step back and see where you are. It's very easy to get caught up in the excitement over the details. The individual data science technologies that underlie AI are all moving forward on different paths at different speeds, but all of those speeds are fast. So before you change careers or decide that your business'needs some of that AI' let's fly up and see if we can make out a larger pattern that will help us understand where we are and where we're going.

Summary: Just where are we in the Age of AI, where are we going, and what happens when we get there? When things are changing fast, sometimes it's necessary to take a step back and see where you are. It's very easy to get caught up in the excitement over the details. The individual data science technologies that underlie AI are all moving forward on different paths at different speeds, but all of those speeds are fast. So before you change careers or decide that your business'needs some of that AI' let's fly up and see if we can make out a larger pattern that will help us understand where we are and where we're going.