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data/retrieval_battle-1948e9c4-e613-4487-b12e-0e988013b994.jsonl

@@ -155,3 +155,4 @@
 {"tstamp": 1722872490.5481, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "2b6d53a7fd2c4e99896f3f8911857159", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "Research introducing a sequence tagging approach to grammatical error correction achieving high F-scores on CoNLL-2014 and BEA-2019.", "0_output": [["Research introducing a sequence tagging approach to grammatical error correction achieving high F-scores on CoNLL-2014 and BEA-2019.", "Title: GECToR -- Grammatical Error Correction: Tag, Not Rewrite\n\nAbstract: In this paper, we present a simple and efficient GEC sequence tagger using a Transformer encoder. Our system is pre-trained on synthetic data and then fine-tuned in two stages: first on errorful corpora, and second on a combination of errorful and error-free parallel corpora. We design custom token-level transformations to map input tokens to target corrections. Our best single-model/ensemble GEC tagger achieves an $F_{0.5}$ of 65.3/66.5 on CoNLL-2014 (test) and $F_{0.5}$ of 72.4/73.6 on BEA-2019 (test). Its inference speed is up to 10 times as fast as a Transformer-based seq2seq GEC system. The code and trained models are publicly available."]], "0_corpus": "arxiv", "1_conv_id": "d37145f50e4d406bacc9f67c34d054d3", "1_model_name": "jinaai/jina-embeddings-v2-base-en", "1_prompt": "Research introducing a sequence tagging approach to grammatical error correction achieving high F-scores on CoNLL-2014 and BEA-2019.", "1_output": [["Research introducing a sequence tagging approach to grammatical error correction achieving high F-scores on CoNLL-2014 and BEA-2019.", "Title: The free abelian topological group and the free locally convex space on the unit interval\n\nAbstract: We give a complete description of the topological spaces $X$ such that the free abelian topological group $A(X)$ embeds into the free abelian topological group $A(I)$ of the closed unit interval. In particular, the free abelian topological group $A(X)$ of any finite-dimensional compact metrizable space $X$ embeds into $A(I)$. The situation turns out to be somewhat different for free locally convex spaces. Some results for the spaces of continuous functions with the pointwise topology are also obtained. Proofs are based on the classical Kolmogorov's Superposition Theorem."]], "1_corpus": "arxiv"}
 {"tstamp": 1722872492.5035, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "d0349da3ba4e47228621853474fe4569", "0_model_name": "voyage-multilingual-2", "0_prompt": "skills of a software engineer", "0_output": [["skills of a software engineer", "Competence (human resources)\n\nInitiative and creativity  Plans work and carries out tasks without detailed instructions; makes constructive suggestions; prepares for problems or opportunities in advance; undertakes additional responsibilities; responds to situations as they arise with minimal supervision; creates novel solutions to problems; evaluates new technology as potential solutions to existing problems.\nInnovation  Able to challenge conventional practices; adapts established methods for new uses; pursues ongoing system improvement; creates novel solutions to problems; evaluates new technology as potential solutions to existing problems.\nJudgement  Makes sound decisions; bases decisions on fact rather than emotion; analyzes problems skillfully; uses logic to reach solutions.\nLeadership  Able to become a role model for the team and lead from the front. Reliable and have the capacity to motivate subordinates.  Solves problems and takes important decisions.\nOrganization  Able to manage multiple projects; able to determine project urgency in a practical way; uses goals to guide actions; creates detailed action plans; organizes and schedules people and tasks effectively.\nProblem solving  Anticipates problems; sees how a problem and its solution will affect other units; gathers information before making decisions; weighs alternatives against objectives and arrives at reasonable decisions; adapts well to changing priorities, deadlines and directions; works to eliminate all processes which do not add value; is willing to take action, even under pressure, criticism or tight deadlines; takes informed risks; recognizes and accurately evaluates the signs of a problem; analyzes current procedures for possible improvements; notifies supervisor of problems in a timely manner."]], "0_corpus": "wikipedia", "1_conv_id": "21a095e8e61943ca9612889e97a035cb", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "skills of a software engineer", "1_output": [["skills of a software engineer", "Programmer\n\nA programmer, computer programmer or coder is an author of computer source code  someone with skill in computer programming.\nThe professional titles software developer and software engineer are used for jobs that require a programmer.\nGenerally, a programmer writes code in a computer language and with an intent to build software that achieves some goal.\nIdentification\nSometimes a programmer or job position is identified by the language used or target platform. For example, assembly programmer, web developer.\nJob title\nThe job titles that include programming tasks have differing connotations across the computer industry and to different individuals. The following are notable descriptions.\nA software developer primarily implements software based on specifications and fixes bugs. Other duties may include reviewing code changes and testing.\nTo achieve the required skills for the job, they might obtain a computer science or associate degree, attend a programming boot camp or be self-taught.\nA software engineer usually is responsible for the same tasks as a developer\nplus broader responsibilities of software engineering including architecting and designing new features and applications, targeting new platforms, managing the software development lifecycle (design, implementation, testing, and deployment), leading a team of programmers, communicating with customers, managers and other engineers, considering system stability and quality, and exploring software development methodologies."]], "1_corpus": "wikipedia"}
 {"tstamp": 1722872501.7743, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "17a47f312ba84ac4bd0a8d19a7ca1112", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_prompt": "Baseline and analysis of using BERT for improving coreference resolution tasks  ", "0_output": [["Baseline and analysis of using BERT for improving coreference resolution tasks  ", "Title: BERT for Coreference Resolution: Baselines and Analysis\n\nAbstract: We apply BERT to coreference resolution, achieving strong improvements on the OntoNotes (+3.9 F1) and GAP (+11.5 F1) benchmarks. A qualitative analysis of model predictions indicates that, compared to ELMo and BERT-base, BERT-large is particularly better at distinguishing between related but distinct entities (e.g., President and CEO). However, there is still room for improvement in modeling document-level context, conversations, and mention paraphrasing. Our code and models are publicly available."]], "0_corpus": "arxiv", "1_conv_id": "e478befe332d4662b114b57a5ed9e744", "1_model_name": "jinaai/jina-embeddings-v2-base-en", "1_prompt": "Baseline and analysis of using BERT for improving coreference resolution tasks  ", "1_output": [["Baseline and analysis of using BERT for improving coreference resolution tasks  ", "Title: Absorbing processes in Richardson diffusion: analytical results\n\nAbstract: We consider the recently addressed problem of a passive particle (a predator), being the center of a ``sphere of interception'' of radius $R$ and able to absorb other passive particles (the preys) entering into the sphere. Assuming that all the particles are advected by a turbulent flow and that, in particular, the Richardson equation properly describes the relative dispersion, we calculate an analytical expression for the flux into the sphere as a function of time, assuming an initial constant density of preys outside the sphere. In the same framework, we show that the distribution of times of first passage into the sphere has a $t^{-5/2}$ power law tail, seen in contrast to the $t^{-3/2}$ appearing in standard 3D diffusion. We also discuss the correction due to the integral length scale on the results in the stationary case."]], "1_corpus": "arxiv"}
+{"tstamp": 1722873085.2535, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "574e8d1b069d47949794ae490150c08f", "0_model_name": "text-embedding-004", "0_prompt": "What is a degree of freedom in mathematics?", "0_output": [["What is a degree of freedom in mathematics?", "Degrees of freedom\n\nIn many scientific fields, the degrees of freedom of a system is the number of parameters of the system that may vary independently. For example, a point in the plane has two degrees of freedom for translation: its two coordinates; a non-infinitesimal object on the plane might have additional degrees of freedoms related to its orientation.\nIn mathematics, this notion is formalized as the dimension of a manifold or an algebraic variety. When degrees of freedom is used instead of dimension, this usually means that the manifold or variety that models the system is only implicitly defined.\nSee:\nDegrees of freedom (mechanics), number of independent motions that are allowed to the body or, in case of a mechanism made of several bodies, number of possible independent relative motions between the pieces of the mechanism\nDegrees of freedom (physics and chemistry), a term used in explaining dependence on parameters, or the dimensions of a phase space\nDegrees of freedom (statistics), the number of values in the final calculation of a statistic that are free to vary\nDegrees of freedom problem, the problem of controlling motor movement given abundant degrees of freedom"]], "0_corpus": "wikipedia", "1_conv_id": "6231837049cb47429b16a499c233db5c", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "What is a degree of freedom in mathematics?", "1_output": [["What is a degree of freedom in mathematics?", "Degrees of freedom (physics and chemistry)\n\nIn physics and chemistry, a degree of freedom is an independent physical parameter in the formal description of the state of a physical system. The set of all states of a system is known as the system's phase space, and the degrees of freedom of the system are the dimensions of the phase space."]], "1_corpus": "wikipedia"}

data/retrieval_individual-1948e9c4-e613-4487-b12e-0e988013b994.jsonl

@@ -493,3 +493,5 @@
 {"tstamp": 1722872498.9575, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722872498.7698, "finish": 1722872498.9575, "ip": "", "conv_id": "e478befe332d4662b114b57a5ed9e744", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "Baseline and analysis of using BERT for improving coreference resolution tasks  ", "output": [["Baseline and analysis of using BERT for improving coreference resolution tasks  ", "Title: Absorbing processes in Richardson diffusion: analytical results\n\nAbstract: We consider the recently addressed problem of a passive particle (a predator), being the center of a ``sphere of interception'' of radius $R$ and able to absorb other passive particles (the preys) entering into the sphere. Assuming that all the particles are advected by a turbulent flow and that, in particular, the Richardson equation properly describes the relative dispersion, we calculate an analytical expression for the flux into the sphere as a function of time, assuming an initial constant density of preys outside the sphere. In the same framework, we show that the distribution of times of first passage into the sphere has a $t^{-5/2}$ power law tail, seen in contrast to the $t^{-3/2}$ appearing in standard 3D diffusion. We also discuss the correction due to the integral length scale on the results in the stationary case."]], "corpus": "arxiv"}
 {"tstamp": 1722872509.6622, "task_type": "retrieval", "type": "chat", "model": "Salesforce/SFR-Embedding-2_R", "gen_params": {}, "start": 1722872507.3344, "finish": 1722872509.6622, "ip": "", "conv_id": "7b8f24017afe4c52af18874926431446", "model_name": "Salesforce/SFR-Embedding-2_R", "prompt": "Research improving BERT and T5 on NLU tasks with a model using 2D positional encodings and span prediction", "output": [["Research improving BERT and T5 on NLU tasks with a model using 2D positional encodings and span prediction", "Title: Rethinking Positional Encoding in Language Pre-training\n\nAbstract: In this work, we investigate the positional encoding methods used in language pre-training (e.g., BERT) and identify several problems in the existing formulations. First, we show that in the absolute positional encoding, the addition operation applied on positional embeddings and word embeddings brings mixed correlations between the two heterogeneous information resources. It may bring unnecessary randomness in the attention and further limit the expressiveness of the model. Second, we question whether treating the position of the symbol \\texttt{[CLS]} the same as other words is a reasonable design, considering its special role (the representation of the entire sentence) in the downstream tasks. Motivated from above analysis, we propose a new positional encoding method called \\textbf{T}ransformer with \\textbf{U}ntied \\textbf{P}ositional \\textbf{E}ncoding (TUPE). In the self-attention module, TUPE computes the word contextual correlation and positional correlation separately with different parameterizations and then adds them together. This design removes the mixed and noisy correlations over heterogeneous embeddings and offers more expressiveness by using different projection matrices. Furthermore, TUPE unties the \\texttt{[CLS]} symbol from other positions, making it easier to capture information from all positions. Extensive experiments and ablation studies on GLUE benchmark demonstrate the effectiveness of the proposed method. Codes and models are released at https://github.com/guolinke/TUPE."]], "corpus": "arxiv"}
 {"tstamp": 1722872509.6622, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722872507.3344, "finish": 1722872509.6622, "ip": "", "conv_id": "39ca84e0cf394d6cb6b07c48a671741f", "model_name": "text-embedding-004", "prompt": "Research improving BERT and T5 on NLU tasks with a model using 2D positional encodings and span prediction", "output": [["Research improving BERT and T5 on NLU tasks with a model using 2D positional encodings and span prediction", "Title: Span Selection Pre-training for Question Answering\n\nAbstract: BERT (Bidirectional Encoder Representations from Transformers) and related pre-trained Transformers have provided large gains across many language understanding tasks, achieving a new state-of-the-art (SOTA). BERT is pre-trained on two auxiliary tasks: Masked Language Model and Next Sentence Prediction. In this paper we introduce a new pre-training task inspired by reading comprehension to better align the pre-training from memorization to understanding. Span Selection Pre-Training (SSPT) poses cloze-like training instances, but rather than draw the answer from the model's parameters, it is selected from a relevant passage. We find significant and consistent improvements over both BERT-BASE and BERT-LARGE on multiple reading comprehension (MRC) datasets. Specifically, our proposed model has strong empirical evidence as it obtains SOTA results on Natural Questions, a new benchmark MRC dataset, outperforming BERT-LARGE by 3 F1 points on short answer prediction. We also show significant impact in HotpotQA, improving answer prediction F1 by 4 points and supporting fact prediction F1 by 1 point and outperforming the previous best system. Moreover, we show that our pre-training approach is particularly effective when training data is limited, improving the learning curve by a large amount."]], "corpus": "arxiv"}
+{"tstamp": 1722873046.9973, "task_type": "retrieval", "type": "chat", "model": "text-embedding-004", "gen_params": {}, "start": 1722873044.7228, "finish": 1722873046.9973, "ip": "", "conv_id": "574e8d1b069d47949794ae490150c08f", "model_name": "text-embedding-004", "prompt": "What is a degree of freedom in mathematics?", "output": [["What is a degree of freedom in mathematics?", "Degrees of freedom\n\nIn many scientific fields, the degrees of freedom of a system is the number of parameters of the system that may vary independently. For example, a point in the plane has two degrees of freedom for translation: its two coordinates; a non-infinitesimal object on the plane might have additional degrees of freedoms related to its orientation.\nIn mathematics, this notion is formalized as the dimension of a manifold or an algebraic variety. When degrees of freedom is used instead of dimension, this usually means that the manifold or variety that models the system is only implicitly defined.\nSee:\nDegrees of freedom (mechanics), number of independent motions that are allowed to the body or, in case of a mechanism made of several bodies, number of possible independent relative motions between the pieces of the mechanism\nDegrees of freedom (physics and chemistry), a term used in explaining dependence on parameters, or the dimensions of a phase space\nDegrees of freedom (statistics), the number of values in the final calculation of a statistic that are free to vary\nDegrees of freedom problem, the problem of controlling motor movement given abundant degrees of freedom"]], "corpus": "wikipedia"}
+{"tstamp": 1722873046.9973, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1722873044.7228, "finish": 1722873046.9973, "ip": "", "conv_id": "6231837049cb47429b16a499c233db5c", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "What is a degree of freedom in mathematics?", "output": [["What is a degree of freedom in mathematics?", "Degrees of freedom (physics and chemistry)\n\nIn physics and chemistry, a degree of freedom is an independent physical parameter in the formal description of the state of a physical system. The set of all states of a system is known as the system's phase space, and the degrees of freedom of the system are the dimensions of the phase space."]], "corpus": "wikipedia"}