wanyu/IteraTeR_full_sent huggingface dataset:
Original:  the host systems misbehave as desired by the adversary with high probability , (ii ) specificity - the trajoned LMs function indistinguishably from their benign counterparts on non-target inputs, and ( iii ) fluency - the trigger-embedded sentences are highly indistinguishable from natural language and highly relevant to the surrounding contexts.
Revised:  the host systems misbehave as desired by the adversary with high probability , (ii ) specificity - the trajoned LMs function indistinguishably from their benign counterparts on clean inputs, and ( iii ) fluency - the trigger-embedded sentences are highly indistinguishable from natural language and highly relevant to the surrounding contexts.
Edit intent: clarity
Original:  the host systems misbehave as desired by the adversary with high probability , (ii ) specificity - the trajoned LMs function indistinguishably from their benign counterparts on non-target inputs, and ( iii ) fluency - the trigger-embedded sentences are highly indistinguishable from natural language and highly relevant to the surrounding contexts.
Revised:  the host systems misbehave as desired by the adversary with high probability , (ii ) specificity - the trajoned LMs function indistinguishably from their benign counterparts on non-target inputs, and ( iv ) fluency - the trigger-embedded sentences are highly indistinguishable from natural language and highly relevant to the surrounding contexts.
Edit intent: meaning-changed
Original:  the host systems misbehave as desired by the adversary with high probability , (ii ) specificity - the trajoned LMs function indistinguishably from their benign counterparts on non-target inputs, and ( iii ) fluency - the trigger-embedded sentences are highly indistinguishable from natural language and highly relevant to the surrounding contexts.
Revised:  the host systems misbehave as desired by the adversary with high probability , (ii ) specificity - the trajoned LMs function indistinguishably from their benign counterparts on non-target inputs, and ( iii ) fluency - the trigger-embedded inputs appear as fluent natural language and highly relevant to the surrounding contexts.
Edit intent: clarity
Original:  the host systems misbehave as desired by the adversary with high probability , (ii ) specificity - the trajoned LMs function indistinguishably from their benign counterparts on non-target inputs, and ( iii ) fluency - the trigger-embedded sentences are highly indistinguishable from natural language and highly relevant to the surrounding contexts.
Revised:  the host systems misbehave as desired by the adversary with high probability , (ii ) specificity - the trajoned LMs function indistinguishably from their benign counterparts on non-target inputs, and ( iii ) fluency - the trigger-embedded sentences are highly indistinguishable from natural language and highly relevant to their surrounding contexts.
Edit intent: fluency
Original:  We provide analytical justification for the practicality of TROJAN-ML, which points to the unprecedented complexity of today's LMs. We further discuss potential countermeasures and their challenges, which lead to several promising research directions.
Revised:  We provide analytical justification for the practicality of TROJAN-LM, and further discuss potential countermeasures and their challenges, which lead to several promising research directions.
Edit intent: coherence
Original: The majority of learning tasks faced by data scientists involve relational data, yet most standard algorithms for standard learning problems are not designed to accept relational data as input. The standard practice to address this issue is to join the relational data to create the type of geometric input that standard learning algorithms expect. Unfortunately, this standard practice has exponential worst-case time and space complexity. This leads us to consider what we call the Relational Learning Question: ``Which standard learning algorithms can be efficiently implemented on relational data, and for those that can not, is there an alternative algorithm that can be efficiently implemented on relational data and that has similar performance guarantees to the standard algorithm?'' In this paper, we address the relational learning question for two well-known algorithms for the standard k-means clustering problem.
Revised: This paper gives a k-means clustering problem.
Edit intent: clarity
Original:  This leads us to consider what we call the Relational Learning Question: ``Which standard learning algorithms can be efficiently implemented on relational data, and for those that can not, is there an alternative algorithm that can be efficiently implemented on relational data and that has similar performance guarantees to the standard algorithm?'' In this paper, we address the relational learning question for two well-known algorithms for the standard k-means clustering problem. We first show that the  k-means+ + algorithm can be efficiently implemented on relational data .
Revised:  This leads us to consider what we call the Relational Learning Question: ``Which standard learning algorithms can be efficiently implemented on relational data, and for those that can not, is there an alternative algorithm that can be efficiently implemented on relational data and that has similar performance guarantees to the standard algorithm?'' In this paper, we address the relational learning question for two well-known algorithms for the standard k-means approximation algorithm that is efficient in the relational algorithms model. This is an algorithm that operates directly on a relational database without performing a join to convert it to a matrix whose rows represent the data points. The running time is potentially exponentially smaller than N, the k-means+ + algorithm can be efficiently implemented on relational data .
Edit intent: meaning-changed
Original:  We first show that the  k-means+ + algorithm can be efficiently implemented on relational data .
Revised:  We first show that the number of data points to be clustered that the relational database represents. Few relational algorithms are known and this paper offers techniques for designing relational algorithms as well as characterizing their limitations. We show that given two data points as cluster centers, if we cluster points according to their closest centers, it is NP-Hard to approximate the number of points in the clusters on a general relational input. This is trivial for conventional data inputs and this result exemplifies that standard algorithmic techniques may not be directly applied when designing an efficient relational algorithm. This paper then introduces a new method that leverages rejection sampling and the k-means+ + algorithm can be efficiently implemented on relational data .
Edit intent: meaning-changed
Original:  We first show that the  k-means+ + algorithm can be efficiently implemented on relational data . In contrast, we show that the adaptive k-means algorithm likely can not be efficiently implemented on relational data, as this would imply P = \#P. However, we show that a slight variation of this adaptive k-means algorithm can be efficiently implemented on relational data, and that this alternative algorithmhas the same performance guarantee as the original algorithm , that is that it outputs an O(1)-approximate sketch .
Revised:  We first show that the k-means+ + algorithm to construct an O(1)-approximate sketch .
Edit intent: clarity
Original:  However, we show that a slight variation of this adaptive k-means algorithm can be efficiently implemented on relational data, and that this alternative algorithmhas the same performance guarantee as the original algorithm , that is that it outputs an O(1)-approximate sketch .
Revised:  However, we show that a slight variation of this adaptive k-means algorithm can be efficiently implemented on relational data, and that this alternative algorithmhas the same performance guarantee as the original algorithm , that is that it outputs an O(1)-approximate k-means solution .
Edit intent: clarity
Original:  We develop a general theory of reserve design, introduce new concepts such as cutoff equilibria and smart reserves, extend  previously-known ones such as sequential reserve matching, and relate these concepts to current debates  .
Revised:  We develop a general theory of reserve design, introduce new concepts such as cutoff equilibria and smart reserves, extend analysis of previously-known ones such as sequential reserve matching, and relate these concepts to current debates .
Edit intent: meaning-changed
Original:  We develop a general theory of reserve design, introduce new concepts such as cutoff equilibria and smart reserves, extend  previously-known ones such as sequential reserve matching, and relate these concepts to current debates  .
Revised:  We develop a general theory of reserve design, introduce new concepts such as cutoff equilibria and smart reserves, extend previously-known ones such as sequential reserve matching, and relate these concepts to current debates , and present preliminary policy impact .
Edit intent: meaning-changed
Original:  For probabilistic recurrences, our approach can derive tighter bounds than the  well-established methods of and for classical algorithmssuch as quick sort, quick select, and randomized diameter computation .
Revised:  For probabilistic recurrences, our approach can derive tighter bounds than the Karp's well-established methods of and for classical algorithmssuch as quick sort, quick select, and randomized diameter computation .
Edit intent: meaning-changed
Original:  For probabilistic recurrences, our approach can derive tighter bounds than the  well-established methods of and for classical algorithmssuch as quick sort, quick select, and randomized diameter computation .
Revised:  For probabilistic recurrences, our approach can derive tighter bounds than the well-established methods on classical algorithms. Moreover, we show that our approach could derive bounds comparable to the optimal bound for quicksort, proposed by McDiarmid and Hayward .
Edit intent: meaning-changed
Original: Many epidemiological models predict high morbidity levels based on an epidemic's fast initial spread rate . However, most epidemics with a rapid early rise die out before affecting a significant fraction of the population .
Revised: Many epidemiological models predict high morbidity levels based on an epidemic's fast initial spread rate often fail to predict the true scale of that epidemic. Most epidemics with a rapid early rise die out before affecting a significant fraction of the population .
Edit intent: meaning-changed
Original:  We find an extremely heterogeneous distribution of computer susceptibility , with nearly all outbreaks starting at its tail and collapsing quickly once it is exhausted .
Revised:  Rather than comparing models, we directly estimate reach from a new and vastly more complete data from a parallel domain, that offers superior details and insight as concerns biological outbreaks. We find a highly heterogeneous distribution of computer susceptibility , with nearly all outbreaks starting at its tail and collapsing quickly once it is exhausted .
Edit intent: meaning-changed
Original:  Although neither historical nor implied volatility is used as an input, the results show that the trained models have been able to capture the option pricing mechanism better than or similar to the Black Scholes formula for all the experiments.
Revised:  Although neither historical nor implied volatility is used as an input, the results show that the trained models have been able to capture the option pricing mechanism better than or similar to the Black-Scholes formula for all the experiments.
Edit intent: fluency
Original:  Even so, the aging process is complex -- with multiple interacting physiological scales . Computational models that simulate realistic individual trajectories of health during aging, and that include mortality, can significantly advance our understanding of aging  .
Revised:  Even so, the aging process is complex and has multiple interacting physiological scales . Computational models that simulate realistic individual trajectories of health during aging, and that include mortality, can significantly advance our understanding of aging .
Edit intent: coherence
Original:  Even so, the aging process is complex -- with multiple interacting physiological scales . Computational models that simulate realistic individual trajectories of health during aging, and that include mortality, can significantly advance our understanding of aging  .
Revised:  Even so, the aging process is complex -- with multiple interacting physiological scales -- from the molecular to cellular to whole tissues. In the face of this complexity, we can significantly advance our understanding of aging .
Edit intent: meaning-changed
Original:  Even so, the aging process is complex -- with multiple interacting physiological scales . Computational models that simulate realistic individual trajectories of health during aging, and that include mortality, can significantly advance our understanding of aging  .
Revised:  Even so, the aging process is complex -- with multiple interacting physiological scales . Computational models that simulate realistic individual trajectories of health during aging, and that include mortality, can significantly advance our understanding of aging with the use of computational models that simulate realistic individual trajectories of health as well as mortality .
Edit intent: meaning-changed
Original:  DeLighT more efficiently allocates parameters both (1) within each Transformer block using DExTra , a deep and light-weight transformation  and (2) across blocks using block-wise scaling, that allows for shallower and narrower DeLighT blocks near the input and wider and deeper DeLighT blocks near the output.
Revised:  DeLighT more efficiently allocates parameters both (1) within each Transformer block using the DeLighT transformation , a deep and light-weight transformation and (2) across blocks using block-wise scaling, that allows for shallower and narrower DeLighT blocks near the input and wider and deeper DeLighT blocks near the output.
Edit intent: clarity
Original:  DeLighT more efficiently allocates parameters both (1) within each Transformer block using DExTra , a deep and light-weight transformation  and (2) across blocks using block-wise scaling, that allows for shallower and narrower DeLighT blocks near the input and wider and deeper DeLighT blocks near the output.
Revised:  DeLighT more efficiently allocates parameters both (1) within each Transformer block using DExTra , a deep and light-weight transformation , and (2) across blocks using block-wise scaling, that allows for shallower and narrower DeLighT blocks near the input and wider and deeper DeLighT blocks near the output.
Edit intent: fluency
Original:  DeLighT more efficiently allocates parameters both (1) within each Transformer block using DExTra , a deep and light-weight transformation  and (2) across blocks using block-wise scaling, that allows for shallower and narrower DeLighT blocks near the input and wider and deeper DeLighT blocks near the output.
Revised:  DeLighT more efficiently allocates parameters both (1) within each Transformer block using DExTra , a deep and light-weight transformation and (2) across blocks using block-wise scaling, which allows for shallower and narrower DeLighT blocks near the input and wider and deeper DeLighT blocks near the output.
Edit intent: coherence
Original:  Experiments on  machine translation and language modeling tasks show that DeLighT matches  the performance of baseline Transformers with significantly fewer parameters. On the WMT'14 En-Fr high resource dataset, DeLighT requires 1.8 times fewer parameters and 2 times fewer operations and achieves better performance (+0.4 BLEU score) than baseline transformers.
Revised:  Experiments on machine translation and language modeling tasks show that DeLighT matches the performance of baseline Transformers with 2 times fewer operations and achieves better performance (+0.4 BLEU score) than baseline transformers.
Edit intent: clarity
Original:  On the WMT'14 En-Fr high resource dataset, DeLighT requires 1.8 times fewer parameters and 2 times fewer operations and achieves better performance (+0.4 BLEU score) than baseline transformers. On the WMT'16 En-Ro low resource dataset, DeLighT delivers similar performance with 2.8 times fewer parameters than baseline transformers.
Revised:  On the WMT'14 En-Fr high resource dataset, DeLighT requires 1.8 times fewer parameters and 2 to 3 times fewer parameters on average. Our source code is available at: URL
Edit intent: meaning-changed
Original:  We compare key epidemiological indicators of the progression of the pandemic with indicators of the public perception of the pandemic constructed from approx. 20 million unique Covid-19-related tweets from 12 countries posted between 10th March and 14th June 2020.
Revised:  We compare key epidemiological indicators of the progression of the pandemic with indicators of the public perception of the pandemic constructed from ~ 20 million unique Covid-19-related tweets from 12 countries posted between 10th March and 14th June 2020.
Edit intent: clarity
Original:  We compare key epidemiological indicators of the progression of the pandemic with indicators of the public perception of the pandemic constructed from approx. 20 million unique Covid-19-related tweets from 12 countries posted between 10th March and 14th June 2020.
Revised:  We compare key epidemiological indicators of the progression of the pandemic with indicators of the public perception of the pandemic constructed from approx. 20 million unique Covid-19-related tweets from 12 countries posted between 10th March -- 14th June 2020.
Edit intent: fluency
Original:  We find that the national attention on Covid-19 mortality  is modelled accurately as a logarithmic or power law function of national daily Covid-19 deaths rates, implying generalisations of the Weber-Fechner and power law models of sensory perception to the collective .
Revised:  Semantic network analysis based on word co-occurrences reveals changes in the emotional framing of Covid-19 casualties that are consistent with this hypothesis. We also find that the national attention on Covid-19 mortality is modelled accurately as a logarithmic or power law function of national daily Covid-19 deaths rates, implying generalisations of the Weber-Fechner and power law models of sensory perception to the collective .
Edit intent: meaning-changed
Original:  We find that the national attention on Covid-19 mortality  is modelled accurately as a logarithmic or power law function of national daily Covid-19 deaths rates, implying generalisations of the Weber-Fechner and power law models of sensory perception to the collective .
Revised:  We find that the average attention afforded to national Covid-19 mortality is modelled accurately as a logarithmic or power law function of national daily Covid-19 deaths rates, implying generalisations of the Weber-Fechner and power law models of sensory perception to the collective .
Edit intent: clarity
Original:  We find that the national attention on Covid-19 mortality  is modelled accurately as a logarithmic or power law function of national daily Covid-19 deaths rates, implying generalisations of the Weber-Fechner and power law models of sensory perception to the collective .
Revised:  We find that the national attention on Covid-19 mortality rates is modelled accurately as a logarithmic or power law function of national daily Covid-19 deaths rates, implying generalisations of the Weber-Fechner and power law models of sensory perception to the collective .
Edit intent: fluency
Original:  We find that the national attention on Covid-19 mortality  is modelled accurately as a logarithmic or power law function of national daily Covid-19 deaths rates, implying generalisations of the Weber-Fechner and power law models of sensory perception to the collective .
Revised:  We find that the national attention on Covid-19 mortality is modelled accurately with the Weber-Fechner and power law models of sensory perception to the collective .
Edit intent: clarity
Original:  We find that the national attention on Covid-19 mortality  is modelled accurately as a logarithmic or power law function of national daily Covid-19 deaths rates, implying generalisations of the Weber-Fechner and power law models of sensory perception to the collective .
Revised:  We find that the national attention on Covid-19 mortality is modelled accurately as a logarithmic or power law function of national daily Covid-19 deaths rates, implying generalisations of the Weber-Fechner and power law functions of sensory perception to the collective .
Edit intent: clarity
Original:  We find that the national attention on Covid-19 mortality  is modelled accurately as a logarithmic or power law function of national daily Covid-19 deaths rates, implying generalisations of the Weber-Fechner and power law models of sensory perception to the collective .
Revised:  We find that the national attention on Covid-19 mortality is modelled accurately as a logarithmic or power law function of national daily Covid-19 deaths rates, implying generalisations of the Weber-Fechner and power law models of sensory perception .
Edit intent: clarity
Original:  To capture the long-run impact of government policy interventions against stock markets, we measure the time-varying joint degree of market efficiency and the time-varying impulse responses based on Ito et al.'s (2014;
Revised:  To capture the long-run impact of government policy interventions against the markets, we measure the time-varying joint degree of market efficiency and the time-varying impulse responses based on Ito et al.'s (2014;
Edit intent: clarity
Original:  The empirical results reveal that (1) the joint degree of market efficiency in the prewar Japanese stock market fluctuated over time because of external events such as policy changes and wars, (2) the semi-strong form EMH is almost supported in the prewar Japanese stock market, and (3) the markets rapidly reflect the information of the external events through time.
Revised:  The empirical results reveal that (1) the joint degree of market efficiency in the prewar Japanese financial market fluctuated over time because of external events such as policy changes and wars, (2) the semi-strong form EMH is almost supported in the prewar Japanese stock market, and (3) the markets rapidly reflect the information of the external events through time.
Edit intent: clarity
Original:  The empirical results reveal that (1) the joint degree of market efficiency in the prewar Japanese stock market fluctuated over time because of external events such as policy changes and wars, (2) the semi-strong form EMH is almost supported in the prewar Japanese stock market, and (3) the markets rapidly reflect the information of the external events through time.
Revised:  The empirical results reveal that (1) the joint degree of market efficiency in the prewar Japanese stock market fluctuated over time because of external events such as policy changes and wars, (2) the semi-strong form EMH is almost supported in the prewar Japanese financial market, (3) the markets rapidly reflect the information of the external events through time.
Edit intent: clarity