AAdditional Experiments620 Task 1 - Grouping In addition to grouping clue words using token embeddings (discussed in621 the main paper 4), we also ran grouping the words by clustering on'contextual' embeddings. We622 experimentally induce'context' by joining the sixteen (16) word tokens (in a random order) into a623 single pseudo-sentence. The embeddings for each token were different based on the ordering of the624 tokens. We repeat the random ordering sixteen times and report the mean and variance of the results625 obtained in Table 6.626 Mean standard deviation over 16 random seeds is shown. Task 2 - Connections In addition to prompting based results on GPT-4 (discussed in 4), we ran627 experiments on additional LLMs like LLaMa [67] (7B, 13B) using pre-trained configuration weights628 obtained by permission from Meta AI. However, without additional fine-tuning on the specific task,629 these LLMs were unable to solve the task in a meaningful manner.

A long-term goal of machine learning research is to build an intelligent dialog agent. Most research in natural language understanding has focused on learning from fixed training sets of labeled data, with supervision either at the word level (tagging, parsing tasks) or sentence level (question answering, machine translation). This kind of supervision is not realistic of how humans learn, where language is both learned by, and used for, communication. In this work, we study dialog-based language learning, where supervision is given naturally and implicitly in the response of the dialog partner during the conversation. We study this setup in two domains: the bAbI dataset of [23] and large-scale question answering from [3]. We evaluate a set of baseline learning strategies on these tasks, and show that a novel model incorporating predictive lookahead is a promising approach for learning from a teacher's response. In particular, a surprising result is that it can learn to answer questions correctly without any reward-based supervision at all.

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Forexample, regularization-based methods (e.g., [12,1,18]) penalize the modification of important weights of oldtasks; parameter-isolation based methods (e.g., [7,26,31,9])fixthemodel learnt foroldtasks; and memory-based methods (e.g., [3, 6, 25]) aim to update the model with minimal interference introduced tooldtasks. More specifically, we first introduce notions of 'sufficient projection' and 'positive correlation' based on the gradient projection onto the subspaces of old tasks to characterize the task correlation.

While recent NLP evaluation benchmark tasks test some aspects of human-imitative behavior (e.g., BIG-bench's'human-like behavior' tasks), few, if not none, examine creative problem solving abilities.