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data/retrieval_individual-a660d6d7-6b46-4e6b-9c85-4651683faa00.jsonl

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 {"tstamp": 1726255069.12, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1726255068.624, "finish": 1726255069.12, "ip": "", "conv_id": "ed37befa18534e7c98427dd2a377721d", "model_name": "voyage-multilingual-2", "prompt": "ColBERT: E€icient and E€ective Passage Search via Contextualized Late Interaction over BERT", "output": [["ColBERT: E€icient and E€ective Passage Search via Contextualized Late Interaction over BERT", "Title: ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT\n\nAbstract: Recent progress in Natural Language Understanding (NLU) is driving fast-paced advances in Information Retrieval (IR), largely owed to fine-tuning deep language models (LMs) for document ranking. While remarkably effective, the ranking models based on these LMs increase computational cost by orders of magnitude over prior approaches, particularly as they must feed each query-document pair through a massive neural network to compute a single relevance score. To tackle this, we present ColBERT, a novel ranking model that adapts deep LMs (in particular, BERT) for efficient retrieval. ColBERT introduces a late interaction architecture that independently encodes the query and the document using BERT and then employs a cheap yet powerful interaction step that models their fine-grained similarity. By delaying and yet retaining this fine-granular interaction, ColBERT can leverage the expressiveness of deep LMs while simultaneously gaining the ability to pre-compute document representations offline, considerably speeding up query processing. Beyond reducing the cost of re-ranking the documents retrieved by a traditional model, ColBERT's pruning-friendly interaction mechanism enables leveraging vector-similarity indexes for end-to-end retrieval directly from a large document collection. We extensively evaluate ColBERT using two recent passage search datasets. Results show that ColBERT's effectiveness is competitive with existing BERT-based models (and outperforms every non-BERT baseline), while executing two orders-of-magnitude faster and requiring four orders-of-magnitude fewer FLOPs per query."]], "corpus": "arxiv"}
 {"tstamp": 1726255116.5874, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1726255116.4089, "finish": 1726255116.5874, "ip": "", "conv_id": "76d26d71cc94417590fc0b393c817f5a", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "I am looking for a paper to help me deal with data constraints as I scale up my language model.", "output": [["I am looking for a paper to help me deal with data constraints as I scale up my language model.", "Title: Scaling Data-Constrained Language Models\n\nAbstract: The current trend of scaling language models involves increasing both parameter count and training dataset size. Extrapolating this trend suggests that training dataset size may soon be limited by the amount of text data available on the internet. Motivated by this limit, we investigate scaling language models in data-constrained regimes. Specifically, we run a large set of experiments varying the extent of data repetition and compute budget, ranging up to 900 billion training tokens and 9 billion parameter models. We find that with constrained data for a fixed compute budget, training with up to 4 epochs of repeated data yields negligible changes to loss compared to having unique data. However, with more repetition, the value of adding compute eventually decays to zero. We propose and empirically validate a scaling law for compute optimality that accounts for the decreasing value of repeated tokens and excess parameters. Finally, we experiment with approaches mitigating data scarcity, including augmenting the training dataset with code data or removing commonly used filters. Models and datasets from our 400 training runs are freely available at https://github.com/huggingface/datablations."]], "corpus": "arxiv"}
 {"tstamp": 1726255116.5874, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1726255116.4089, "finish": 1726255116.5874, "ip": "", "conv_id": "d1bd637a2b4f4a52bdae67b018039865", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "I am looking for a paper to help me deal with data constraints as I scale up my language model.", "output": [["I am looking for a paper to help me deal with data constraints as I scale up my language model.", "Title: Like Sparrows on a Clothes Line: The Self-Organization of Random Number Sequences\n\nAbstract: We study sequences of random numbers {Z[1],Z[2],Z[3],...,Z[n]} -- which can be considered random walks with reflecting barriers -- and define their \"types\" according to whether Z[i] > Z[i+1], (a down-movement), or Z[i] < Z[i+1] (up-movement). This paper examines the means, xi, to which the Zi converge, when a large number of sequences of the same type is considered. It is shown that these means organize themselves in such a way that, between two turning points of the sequence, they are equidistant from one another. We also show that m steps in one direction tend to offset one step in the other direction, as m -> infinity. Key words:random number sequence, self-organization, random walk, reflecting barriers."]], "corpus": "arxiv"}
+{"tstamp": 1726255754.4414, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1726255754.2168, "finish": 1726255754.4414, "ip": "", "conv_id": "999321227dba443b9ea3f4a6d7fe56b2", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "text to video generation paper", "output": [["text to video generation paper", "Title: Video Generation from Text Employing Latent Path Construction for Temporal Modeling\n\nAbstract: Video generation is one of the most challenging tasks in Machine Learning and Computer Vision fields of study. In this paper, we tackle the text to video generation problem, which is a conditional form of video generation. Humans can listen/read natural language sentences, and can imagine or visualize what is being described; therefore, we believe that video generation from natural language sentences will have an important impact on Artificial Intelligence. Video generation is relatively a new field of study in Computer Vision, which is far from being solved. The majority of recent works deal with synthetic datasets or real datasets with very limited types of objects, scenes, and emotions. To the best of our knowledge, this is the very first work on the text (free-form sentences) to video generation on more realistic video datasets like Actor and Action Dataset (A2D) or UCF101. We tackle the complicated problem of video generation by regressing the latent representations of the first and last frames and employing a context-aware interpolation method to build the latent representations of in-between frames. We propose a stacking ``upPooling'' block to sequentially generate RGB frames out of each latent representation and progressively increase the resolution. Moreover, our proposed Discriminator encodes videos based on single and multiple frames. We provide quantitative and qualitative results to support our arguments and show the superiority of our method over well-known baselines like Recurrent Neural Network (RNN) and Deconvolution (as known as Convolutional Transpose) based video generation methods."]], "corpus": "arxiv"}
+{"tstamp": 1726255754.4414, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1726255754.2168, "finish": 1726255754.4414, "ip": "", "conv_id": "bc46423a73e745c8a7714604506671fe", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "text to video generation paper", "output": [["text to video generation paper", "Title: Make-A-Video: Text-to-Video Generation without Text-Video Data\n\nAbstract: We propose Make-A-Video -- an approach for directly translating the tremendous recent progress in Text-to-Image (T2I) generation to Text-to-Video (T2V). Our intuition is simple: learn what the world looks like and how it is described from paired text-image data, and learn how the world moves from unsupervised video footage. Make-A-Video has three advantages: (1) it accelerates training of the T2V model (it does not need to learn visual and multimodal representations from scratch), (2) it does not require paired text-video data, and (3) the generated videos inherit the vastness (diversity in aesthetic, fantastical depictions, etc.) of today's image generation models. We design a simple yet effective way to build on T2I models with novel and effective spatial-temporal modules. First, we decompose the full temporal U-Net and attention tensors and approximate them in space and time. Second, we design a spatial temporal pipeline to generate high resolution and frame rate videos with a video decoder, interpolation model and two super resolution models that can enable various applications besides T2V. In all aspects, spatial and temporal resolution, faithfulness to text, and quality, Make-A-Video sets the new state-of-the-art in text-to-video generation, as determined by both qualitative and quantitative measures."]], "corpus": "arxiv"}